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West Florida vs Ferris State With the quarterfinals wrapped up, there's only four teams left to decide the 2022 DII football champion. In Week 15 both games will commence on Saturday, Dec. 10. LIVE: FOOTBALL STREAMING ONLINE Version 143 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.rtyrt For the first time, the NCAA Division II Football Championship semifinals come to Golden as Super Region 4 champion Colorado School of Mines hosts Super Region 1 winner Shepherd for the right to a spot in the national final. Saturday's game kicks off at 1:30 p.m. at Marv Kay Stadium and will stream exclusively on ESPN+ (subscription required). A live audio broadcast with the Oredigger crew of Miles Dunklin and Josh Dover will be available for free on the RMAC Network. The winner of Mines-Shepherd will play the winner of West Florida at Ferris State on Saturday, Dec. 17 in the national championship game in McKinney, Texas. Mines is making its eighth overall appearance in the NCAA Championship, all coming since 2004, including four in a row, which is the third-longest active streak in the nation. The Orediggers are matching their deepest postseason run, which came last year, when they ultimately fell at Valdosta State in the semifinals. 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.tryruj 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "eetrtuj 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.ytit 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.rtyrtuj This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:yu Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2. These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 501,088 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures: Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyu Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset. Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article. Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyt Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset. We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).tyu The work is based on the following publications:tuy COVID-19 Open Research Dataset (CORD-19). 2020. Version 2022-11-25 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2022-11-25. doi:10.5281/zenodo.3715506 Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2022-11-25) R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019 I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020) Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, and Kristina Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380 A Web user interface that uses these data to facilitate the COVID-19 literature exploration, can be found here. More details in our peer-reviewed publication here (also here there is an outdated preprint version).tuyt Funding: We acknowledge support of this work by the project "Moving from Big Data Management to Data Science" (MIS 5002437/3) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).tuyt 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.truy Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Gridtuyt (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doi All data is released in accordance with Elexon's disclaimer and reservation of rights. This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.tujty Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.tuyti The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,373,244,490 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (356,005,294 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/tuyt Wolf, Thomas; Debut, Lysandre; Sanh, Victor; Chaumond, Julien; Delangue, Clement; Moi, Anthony; Cistac, Perric; Ma, Clara; Jernite, Yacine; Plu, Julien; Xu, Canwen; Le Scao, Teven; Gugger, Sylvain; Drame, Mariama; Lhoest, Quentin; Rush, Alexander M.tut PyTorch 2.0 stack support We are very excited by the newly announced PyTorch 2.0 stack. You can enable torch.compile on any of our models, and get support with the Trainer (and in all our PyTorch examples) by using the torchdynamo training argument. For instance, just add --torchdynamo inductor when launching those examples from the command line. This API is still experimental and may be subject to changes as the PyTorch 2.0 stack matures. Note that to get the best performance, we recommend:yht using an Ampere GPU (or more recent) sticking to fixed shaped for now (so use --pad_to_max_length in our examples) Repurpose torchdynamo training args towards torch._dynamo by @sgugger in #20498 Audio Spectrogram Transformer The Audio Spectrogram Transformer model was proposed in AST: Audio Spectrogram Transformer by Yuan Gong, Yu-An Chung, James Glass. The Audio Spectrogram Transformer applies a Vision Transformer to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results for audio classification.tyuity Add Audio Spectogram Transformer by @NielsRogge in #19981 Jukebox The Jukebox model was proposed in Jukebox: A generative model for music by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditionned on an artist, genres and lyrics.tyuti Add Jukebox model (replaces #16875) by @ArthurZucker in #17826 Switch Transformers The SwitchTransformers model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity by William Fedus, Barret Zoph, Noam Shazeer. It is the first MoE model supported in transformers, with the largest checkpoint currently available currently containing 1T parameters.ytrtuj Add Switch transformers by @younesbelkada and @ArthurZucker in #19323 RocBert The RoCBert model was proposed in RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou. It's a pretrained Chinese language model that is robust under various forms of adversarial attacks.tyut Add RocBert by @sww9370 in #20013 CLIPSeg The CLIPSeg model was proposed in Image Segmentation Using Text and Image Prompts by Timo Lüddecke and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen CLIP model for zero- and one-shot image segmentation.rytru NAT was proposed in Neighborhood Attention Transformer by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.tyht It is a hierarchical vision transformer based on Neighborhood Attention, a sliding-window self attention pattern. DiNAT DiNAT was proposed in Dilated Neighborhood Attention Transformer by Ali Hassani and Humphrey Shi. It extends NAT by adding a Dilated Neighborhood Attention pattern to capture global context, and shows significant performance improvements over it.rytu Add Neighborhood Attention Transformer (NAT) and Dilated NAT (DiNAT) models by @alihassanijr in #20219 MobileNetV2 The MobileNet model was proposed in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.tryrtuj add MobileNetV2 model by @hollance in #17845 MobileNetV1 The MobileNet model was proposed in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.tyhu add MobileNetV1 model by @hollance in #17799 Image processors Image processors replace feature extractors as the processing class for computer vision models.rtyhtu Important changes: size parameter is now a dictionary of {"height": h, "width": w}, {"shortest_edge": s}, {"shortest_egde": s, "longest_edge": l} instead of int or tuple. Addition of data_format flag. You can now specify if you want your images to be returned in "channels_first" - NCHW - or "channels_last" - NHWC - format. Processing flags e.g. do_resize can be passed directly to the preprocess method instead of modifying the class attribute: image_processor([image_1, image_2], do_resize=False, return_tensors="pt", data_format="channels_last") Leaving return_tensors unset will return a list of numpy arrays. The classes are backwards compatible and can be created using existing feature extractor configurations - with the size parameter converted.tyr Add Image Processors by @amyeroberts in #19796 Add Donut image processor by @amyeroberts #20425 Add segmentation + object detection image processors by @amyeroberts in #20160 AutoImageProcessor by @amyeroberts in #20111 Backbone for computer vision models We're adding support for a general AutoBackbone class, which turns any vision model (like ConvNeXt, Swin Transformer) into a backbone to be used with frameworks like DETR and Mask R-CNN. The design is in early stages and we welcome feedback.tyu Add AutoBackbone + ResNetBackbone by @NielsRogge in #20229 Improve backbone by @NielsRogge in #20380 [AutoBackbone] Improve API by @NielsRogge in #20407 Support for safetensors offloading If the model you are using has a safetensors checkpoint and you have the library installed, offload to disk will take advantage of this to be more memory efficient and roughly 33% faster.dyhrtju Safetensors offload by @sgugger in #20321 Contrastive search in the generate method Generate: TF contrastive search with XLA support by @gante in #20050 Generate: contrastive search with full optional outputs by @gante in #19963 Breaking changes 🚨 🚨 🚨 Fix Issue 15003: SentencePiece Tokenizers Not Adding Special Tokens in convert_tokens_to_string by @beneyal in #15775 Bugfixes and improvements add dataset by @stevhliu in #20005 Add BERT resources by @stevhliu in #19852 Add LayoutLMv3 resource by @stevhliu in #19932 fix typo by @stevhliu in #20006 Update object detection pipeline to use post_process_object_detection methods by @alaradirik in #20004 clean up vision/text config dict arguments by @ydshieh in #19954 make sentencepiece import conditional in bertjapanesetokenizer by @ripose-jp in #20012 Fix gradient checkpoint test in encoder-decoder by @ydshieh in #20017 Quality by @sgugger in #20002 Update auto processor to check image processor created by @amyeroberts in #20021 [Doctest] Add configuration_deberta_v2.py by @Saad135 in #19995 Improve model tester by @ydshieh in #19984 Fix doctest by @ydshieh in #20023 Show installed libraries and their versions in CI jobs by @ydshieh in #20026 reorganize glossary by @stevhliu in #20010 Now supporting pathlike in pipelines too. by @Narsil in #20030 Add **kwargs by @amyeroberts in #20037 Fix some doctests after PR 15775 by @ydshieh in #20036 [Doctest] Add configuration_camembert.py by @Saad135 in #20039 [Whisper Tokenizer] Make more user-friendly by @sanchit-gandhi in #19921 [FuturWarning] Add futur warning for LEDForSequenceClassification by @ArthurZucker in #19066 fix jit trace error for model forward sequence is not aligned with jit.trace tuple input sequence, update related doc by @sywangyi in #19891 Update esmfold conversion script by @Rocketknight1 in #20028 Fixed torch.finfo issue with torch.fx by @michaelbenayoun in #20040 Only resize embeddings when necessary

Live from the T-Mobile Arena in Paradise, Nevada, Jan Blachowicz and Magomed Ankalaev collide in a light heavyweight championship bout at UFC 282! Currently the third ranked light heavyweight, Blachowicz enters tonight’s match for the vacant title at 29-9, last defeating Aleksandar Rakić in May of 2022 at a UFC on ESPN event. WATCH UFC FIGHT Live ON ESPN+ Our neighbors in the great white north will watch the early prelims on UFC Fight Pass, while the prelims are on TSN and RDS. UFC 282's main card is available on various providers, including BELL and Rogers. UFC 282 fight card Early prelims (6 p.m. ET) on UFC FightPass Billy Quarantillo vs. Alexander Hernandez (Featherweight) T.J. Brown vs. Erik Silva (Featherweight) Vinicius Salvador vs. Daniel da Silva (Flyweight) Cameron Saaiman vs. Steven Koslow (Bantamweight) Prelims (8 p.m. ET) on ESPN2/ESPN Plus Jairzinho Rozenstruik vs. Chris Daukaus (Heavyweight) Raul Rosas Jr. vs. Jay Perrin (Bantamweight) Edmen Shahbazyan vs. Dalcha Lungiambula (Middleweight) Chris Curtis vs. Joaquin Buckley (Middleweight) Main Card (10 p.m. ET) on ESPN Plus Jan Błachowicz vs. Magomed Ankalaev for the vacant UFC Light Heavyweight Championship Paddy Pimblett vs. Jared Gordon (Lightweight) Alex Morono vs. Santiago Ponzinibbio (Catchweight — 180 lb) Darren Till vs. Dricus du Plessis (Middleweight) Bryce Mitchell vs. Ilia Topuria (Featherweight) In the article the new phenomenon of the 20th century – digital nomads in the correlation with historical, traditional nomads analyzed. Digital Nomads - a modern brand, conceptual innovation, symbolizes freedom without boundaries. Digital nomads are the new representatives of modern nomadism, and at the same time they are pronounced Western rationalists.dfhjsag In the West, the satiation with the technical achievements of culture occurred earlier, and there arose some sort of nostalgia about the natural motives. And this induced them to create some sort of illusion for themselves. This illusion exists because their way of thinking remains that of the contemporary people, nevertheless, this is some kind of a challenge. The challenge in itself, above all, at the level of personal development, and also the challenge against the society that imposes some sort of framework, making life very “tight”.rfyrtsdg Over the course of the 21st century, this brand will become even stronger, because the challenges of globalization allow finding unusual solutions. The term ‘nomad’ stands for, on the one hand, one of the ways of surviving and preserving harmony with this world, and, on the other hand, it implies approaching the world in a slightly different perspective.dfhdsgf Afghanistan is the 36th Least Developed Country (LDC) member of the World Trade Organization (WTO). It is a land locked country yet deliberately situated at the heart of Silk Road which even today can fill in as the center point of trade and transit of Central Asia and South Asia. It is accepted that sustainable economic development through drawing in significant investment and trade cannot be accomplished without more extensive integration into the world economy.gjkgfh Afghanistan National Development Strategy (ANDS) unequivocally perceives the role of trade for economic development culminating Afghanistan's reconciliation into the world economy as one of the key advancement objectives for which membership to WTO is a fundamental step (ANDS, 2008).yhtgj Economic growth and reduction in poverty is the main goal of ANDS which place more prominent accentuation on a free market and private sector-led economy. This dissertation highlights on the role of WTO in the guise of TRIPS Agreement in Afghanistan.gfjgfdh The purpose of this study is to apply the experience from a study conducted on entrepreneurial intention among university students with regard to the effective use of triangulation in entrepreneurship research. It applies the knowledge acquired to address issues such as inconsistencies, contradictions, and biases when using the single method. It was also used to develop a framework for research by adopting triangulation. The study discussed issues such as design and the whys and how’s of triangulation.dfyhj It is hoped that the study will help future researchers who adopt triangulation to produce quality work and make informed judgements that lead to completeness. Finally, it would be interesting to researchers who always want to be up-to-date in academic research.fgjkdfy The model used in the publication for Global simulations of multi-frequency HF signal absorption for direct observation of middle atmosphere temperature and composition.dfhgtjtgfujh Die COVID-19-Impfung kann einen Wendepunkt in der Kontrolle der COVID-19-Pandemie darstellen und erfährt daher hohes Maß an öffentlicher Aufmerksamkeit. Einführung und Umsetzung der COVID-19-Impfung gehen mit besonderen Herausforderungen einher, die bei der Impfdatenerfassung zu berücksichtigen sind. In diesem Kontext ist es Ziel des Projekts 'Digitales Impfquoten-Monitoring' (DIM), tagesaktuell, bundesweit die Impfquote zu erfassen und folgend aufbereitet darzustellen, um zeitnah den Verlauf der COVID-19-Impfkampanne zu analysieren, bei Bedarf nach zusteuern, und logistisch bzw. organisatorische Konsequenzen zu ziehen.fyhyjtgh Der durch das DIM-Projekt bereitgestellte Datensatz enthält Daten über den Verlauf der COVID-19 Impfungen in Deutschland. Die hier veröffentlichten Impfdaten aggregieren Daten aus drei Datenquellen:dfhfgjdfyh Die DIM-Daten enthalten Angaben der Impfzentren, mobilen Impfteams, Krankenhäuser und der Betriebsärzte_innen, die über die DIM-Webanwendung übermittelt werden Der täglich aggregierte Kerndatensatz der impfenden Ärzt_innen über die Kassenärztliche Bundesvereinigung (KBV) Der täglich aggregierte Kerndatensatz der impfenden Ärzt_innen über die Privatärztliche Bundesvereinigung (PBV)fdhfgjfh This paper presents the first numerical study on a new concept for the direct measurement of D-region absorption in the HF band. Numerical simulations based on the Appleton–Hartree and Garrett equations of refractive index are presented. Electron temperature as a result of HF radio pumping of the ionosphere is included in the calculations using proper numerical formulation.dfhkjfdhtgh Both O- and X-mode radio wave polarizations are taken into consideration. A global map of HF absorption in the northern hemisphere is calculated. Detailed calculations of HF radio wave absorption as it propagates through the lower atmosphere are presented.sdtgdfhtg The effect of several parameters on the amount of absorption is calculated. The best frequencies to be used for the purpose of this study are discussed. A machine learning model is developed and the capability of the model in estimation of D and E-region constituents includes $N_2$, $O$, $O_2$, as well as $T$ and $N_e$ is examined. Such a technique can also lead to global mapping of HF absorption and improve OTHR (over-the-horizon-radar) performance.dfhfhfjtgg

St. Helena’s Legacy Dance Collective is one of 10 groups signed up to participate in the seventh annual Day of Dance and Cheer, hosted by the Napa High School Spiritleaders on Sunday, Dec. 11. The largest dance event in the county, with more than 500 participating last year, it will be held in Messner Gym starting at noon. Doors open at 11:30 a.m. LIVE: CHEER & DANCE STREAMING ONLINE Version 143 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.rtyrt Hollie Johnson, Napa High School dance director, created the event to showcase all of the talent in the valley and bring unity for those that all share the same passion for dance and cheer. All schools and dance studios are invited to come for free to showcase their favorite routines. Coaches also come for free and are treated to a free lunch. “We love bringing teams together,” Johnson said. “It’s my dancers’ favorite time of year. They always talk about the supportive environment and the new friends they make.” 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.tryruj 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "eetrtuj 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.ytit 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.rtyrtuj This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:yu Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2. These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 501,088 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures: Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyu Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset. Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article. Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyt Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset. We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).tyu The work is based on the following publications:tuy NCA & NDA Northeast Regional Championship 2022 live streaming online Cheer free The American Grand Grand Nationals 2022 live streaming online Cheer free Spirit Cheer Dance Grand Nationals & Cheer 2022 live streaming online Cheer free Encore Baltimore Showdown 2022 live streaming online Cheer free CHEERSPORT Oaks Classic 2022 live streaming online Cheer free Aloha Gatlinburg Showdown 2022 live streaming online Cheer free ASC Battle Under the Big Top Grand National 2022 live streaming online Cheer free Spirit Sports Worcester- National 2022 live streaming online Cheer free UDA DC Dance Challenge 2022 live streaming online Cheer free Nation's Choice Wisconsin Dells Grand National 2022 live streaming online Cheer free CHEERSPORT Greensboro State Classic 2022 live streaming online Cheer free ACP Columbus Showdown 2022 live streaming online Cheer free UCA Salt Lake City Regional 2022 live streaming online Cheer free NCA Holiday Classic 2022 live streaming online Cheer free CHEERSPORT Hot Springs Classic 2022 live streaming online Cheer free All Star Challenge Grand Nationals 2022 live streaming online Cheer free Nation’s Choice Grand Nationals 2022 live streaming online Cheer free The American Grand Nationals 2022 live streaming online Cheer free Global Events Manheim 2022 live streaming online Cheer free AAS Birmingham 2022 live streaming online Cheer free Full Out Combat Cheer Homefront Civil Showdown WA 2022 live streaming online Cheer free Celebrity Championships Branson 2022 live streaming online Cheer free Kingdom Events Manheim 2022 live streaming online Cheer free Maximum Cheer and Dance PA Madness 2022 live streaming online Cheer free World Class Cheer WCC Virtual Championship 2022 live streaming online Cheer free UCE Dayton Experience 2022 live streaming online Cheer free Cheer Derby Nashville Nationals 2022 live streaming online Cheer free Spirit Brands The Festival Wildwood 2022 live streaming online Cheer free US Cheer Productions Holiday Extravaganza Championships 2022 live streaming online Cheer free Deep South Spirit New Jersey Classic 2022 live streaming online Cheer free Gold Rush Fort Worth 2022 live streaming online Cheer free United Cheer Events Galveston Championship 2022 live streaming online Cheer free Spirit Royale Marquee Los Angeles 2022 live streaming online Cheer free MCDA Cowboy Christmas Classic West Monroe LA 2022 live streaming online Cheer free Valley of the Sun Shake Your Palm Palms 2022 live streaming online Cheer free Cheer Evolution Montreal Mayhem 2022 live streaming online Cheer free Baby I’m a Star Christmas Spectacular 2022 live streaming online Cheer free JAMZ Showdown @ The Bay 2022 live streaming online Cheer free 9 Panel Cheer All Star Jam Concord 2022 live streaming online Cheer free Bravo Spirit Christmas Classic 2022 live streaming online Cheer free COVID-19 Open Research Dataset (CORD-19). 2020. Version 2022-11-25 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2022-11-25. doi:10.5281/zenodo.3715506 Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2022-11-25) R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019 I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020) Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, and Kristina Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380 A Web user interface that uses these data to facilitate the COVID-19 literature exploration, can be found here. More details in our peer-reviewed publication here (also here there is an outdated preprint version).tuyt Funding: We acknowledge support of this work by the project "Moving from Big Data Management to Data Science" (MIS 5002437/3) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).tuyt 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.truy Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Gridtuyt (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doi All data is released in accordance with Elexon's disclaimer and reservation of rights. This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.tujty Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.tuyti The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,373,244,490 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (356,005,294 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/tuyt Wolf, Thomas; Debut, Lysandre; Sanh, Victor; Chaumond, Julien; Delangue, Clement; Moi, Anthony; Cistac, Perric; Ma, Clara; Jernite, Yacine; Plu, Julien; Xu, Canwen; Le Scao, Teven; Gugger, Sylvain; Drame, Mariama; Lhoest, Quentin; Rush, Alexander M.tut PyTorch 2.0 stack support We are very excited by the newly announced PyTorch 2.0 stack. You can enable torch.compile on any of our models, and get support with the Trainer (and in all our PyTorch examples) by using the torchdynamo training argument. For instance, just add --torchdynamo inductor when launching those examples from the command line. This API is still experimental and may be subject to changes as the PyTorch 2.0 stack matures. Note that to get the best performance, we recommend:yht using an Ampere GPU (or more recent) sticking to fixed shaped for now (so use --pad_to_max_length in our examples) Repurpose torchdynamo training args towards torch._dynamo by @sgugger in #20498 Audio Spectrogram Transformer The Audio Spectrogram Transformer model was proposed in AST: Audio Spectrogram Transformer by Yuan Gong, Yu-An Chung, James Glass. The Audio Spectrogram Transformer applies a Vision Transformer to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results for audio classification.tyuity Add Audio Spectogram Transformer by @NielsRogge in #19981 Jukebox The Jukebox model was proposed in Jukebox: A generative model for music by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditionned on an artist, genres and lyrics.tyuti Add Jukebox model (replaces #16875) by @ArthurZucker in #17826 Switch Transformers The SwitchTransformers model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity by William Fedus, Barret Zoph, Noam Shazeer. It is the first MoE model supported in transformers, with the largest checkpoint currently available currently containing 1T parameters.ytrtuj Add Switch transformers by @younesbelkada and @ArthurZucker in #19323 RocBert The RoCBert model was proposed in RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou. It's a pretrained Chinese language model that is robust under various forms of adversarial attacks.tyut Add RocBert by @sww9370 in #20013 CLIPSeg The CLIPSeg model was proposed in Image Segmentation Using Text and Image Prompts by Timo Lüddecke and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen CLIP model for zero- and one-shot image segmentation.rytru NAT was proposed in Neighborhood Attention Transformer by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.tyht It is a hierarchical vision transformer based on Neighborhood Attention, a sliding-window self attention pattern. DiNAT DiNAT was proposed in Dilated Neighborhood Attention Transformer by Ali Hassani and Humphrey Shi. It extends NAT by adding a Dilated Neighborhood Attention pattern to capture global context, and shows significant performance improvements over it.rytu Add Neighborhood Attention Transformer (NAT) and Dilated NAT (DiNAT) models by @alihassanijr in #20219 MobileNetV2 The MobileNet model was proposed in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.tryrtuj add MobileNetV2 model by @hollance in #17845 MobileNetV1 The MobileNet model was proposed in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.tyhu add MobileNetV1 model by @hollance in #17799 Image processors Image processors replace feature extractors as the processing class for computer vision models.rtyhtu Important changes: size parameter is now a dictionary of {"height": h, "width": w}, {"shortest_edge": s}, {"shortest_egde": s, "longest_edge": l} instead of int or tuple. Addition of data_format flag. You can now specify if you want your images to be returned in "channels_first" - NCHW - or "channels_last" - NHWC - format. Processing flags e.g. do_resize can be passed directly to the preprocess method instead of modifying the class attribute: image_processor([image_1, image_2], do_resize=False, return_tensors="pt", data_format="channels_last") Leaving return_tensors unset will return a list of numpy arrays. The classes are backwards compatible and can be created using existing feature extractor configurations - with the size parameter converted.tyr Add Image Processors by @amyeroberts in #19796 Add Donut image processor by @amyeroberts #20425 Add segmentation + object detection image processors by @amyeroberts in #20160 AutoImageProcessor by @amyeroberts in #20111 Backbone for computer vision models

How to Champs Sports Cross Country National Championships 2022 Live stream online free LIVE: MARATHON STREAMING ONLINE Version 143 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.rtyrt Val d’Isere, France, is first up, with a men’s giant slalom (10 December) and the first men’s slalom racing of the year (11 December). The French resort is legendary in competitive skiing circles, and home of the great Jean-Claude Killy and his compatriot Henri Oreiller – the ‘madman of the downhill’. This time around it is all about the technical events, however. Henrik Kristoffersen (NOR) will battle against a quality field, including several of his compatriots. 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.tryruj 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "eetrtuj 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.ytit 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.rtyrtuj This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:yu Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2. These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 501,088 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures: Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyu Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset. Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article. Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyt Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset. We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).tyu The work is based on the following publications:tuy COVID-19 Open Research Dataset (CORD-19). 2020. Version 2022-11-25 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2022-11-25. doi:10.5281/zenodo.3715506 Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2022-11-25) R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019 I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020) Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, and Kristina Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380 A Web user interface that uses these data to facilitate the COVID-19 literature exploration, can be found here. More details in our peer-reviewed publication here (also here there is an outdated preprint version).tuyt Funding: We acknowledge support of this work by the project "Moving from Big Data Management to Data Science" (MIS 5002437/3) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).tuyt 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.truy Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Gridtuyt (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doi All data is released in accordance with Elexon's disclaimer and reservation of rights. This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.tujty Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.tuyti The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,373,244,490 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (356,005,294 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/tuyt Wolf, Thomas; Debut, Lysandre; Sanh, Victor; Chaumond, Julien; Delangue, Clement; Moi, Anthony; Cistac, Perric; Ma, Clara; Jernite, Yacine; Plu, Julien; Xu, Canwen; Le Scao, Teven; Gugger, Sylvain; Drame, Mariama; Lhoest, Quentin; Rush, Alexander M.tut PyTorch 2.0 stack support We are very excited by the newly announced PyTorch 2.0 stack. You can enable torch.compile on any of our models, and get support with the Trainer (and in all our PyTorch examples) by using the torchdynamo training argument. For instance, just add --torchdynamo inductor when launching those examples from the command line. This API is still experimental and may be subject to changes as the PyTorch 2.0 stack matures. Note that to get the best performance, we recommend:yht using an Ampere GPU (or more recent) sticking to fixed shaped for now (so use --pad_to_max_length in our examples) Repurpose torchdynamo training args towards torch._dynamo by @sgugger in #20498 Audio Spectrogram Transformer The Audio Spectrogram Transformer model was proposed in AST: Audio Spectrogram Transformer by Yuan Gong, Yu-An Chung, James Glass. The Audio Spectrogram Transformer applies a Vision Transformer to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results for audio classification.tyuity Add Audio Spectogram Transformer by @NielsRogge in #19981 Jukebox The Jukebox model was proposed in Jukebox: A generative model for music by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditionned on an artist, genres and lyrics.tyuti Add Jukebox model (replaces #16875) by @ArthurZucker in #17826 Switch Transformers The SwitchTransformers model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity by William Fedus, Barret Zoph, Noam Shazeer. It is the first MoE model supported in transformers, with the largest checkpoint currently available currently containing 1T parameters.ytrtuj Add Switch transformers by @younesbelkada and @ArthurZucker in #19323 RocBert The RoCBert model was proposed in RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou. It's a pretrained Chinese language model that is robust under various forms of adversarial attacks.tyut Add RocBert by @sww9370 in #20013 CLIPSeg The CLIPSeg model was proposed in Image Segmentation Using Text and Image Prompts by Timo Lüddecke and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen CLIP model for zero- and one-shot image segmentation.rytru NAT was proposed in Neighborhood Attention Transformer by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.tyht It is a hierarchical vision transformer based on Neighborhood Attention, a sliding-window self attention pattern. DiNAT DiNAT was proposed in Dilated Neighborhood Attention Transformer by Ali Hassani and Humphrey Shi. It extends NAT by adding a Dilated Neighborhood Attention pattern to capture global context, and shows significant performance improvements over it.rytu Add Neighborhood Attention Transformer (NAT) and Dilated NAT (DiNAT) models by @alihassanijr in #20219 MobileNetV2 The MobileNet model was proposed in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.tryrtuj add MobileNetV2 model by @hollance in #17845 MobileNetV1 The MobileNet model was proposed in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.tyhu add MobileNetV1 model by @hollance in #17799 Image processors Image processors replace feature extractors as the processing class for computer vision models.rtyhtu Important changes: size parameter is now a dictionary of {"height": h, "width": w}, {"shortest_edge": s}, {"shortest_egde": s, "longest_edge": l} instead of int or tuple. Addition of data_format flag. You can now specify if you want your images to be returned in "channels_first" - NCHW - or "channels_last" - NHWC - format. Processing flags e.g. do_resize can be passed directly to the preprocess method instead of modifying the class attribute: image_processor([image_1, image_2], do_resize=False, return_tensors="pt", data_format="channels_last") Leaving return_tensors unset will return a list of numpy arrays. The classes are backwards compatible and can be created using existing feature extractor configurations - with the size parameter converted.tyr Add Image Processors by @amyeroberts in #19796 Add Donut image processor by @amyeroberts #20425 Add segmentation + object detection image processors by @amyeroberts in #20160 AutoImageProcessor by @amyeroberts in #20111 Backbone for computer vision models We're adding support for a general AutoBackbone class, which turns any vision model (like ConvNeXt, Swin Transformer) into a backbone to be used with frameworks like DETR and Mask R-CNN. The design is in early stages and we welcome feedback.tyu Add AutoBackbone + ResNetBackbone by @NielsRogge in #20229 Improve backbone by @NielsRogge in #20380 [AutoBackbone] Improve API by @NielsRogge in #20407 Support for safetensors offloading If the model you are using has a safetensors checkpoint and you have the library installed, offload to disk will take advantage of this to be more memory efficient and roughly 33% faster.dyhrtju Safetensors offload by @sgugger in #20321 Contrastive search in the generate method Generate: TF contrastive search with XLA support by @gante in #20050 Generate: contrastive search with full optional outputs by @gante in #19963 Breaking changes 🚨 🚨 🚨 Fix Issue 15003: SentencePiece Tokenizers Not Adding Special Tokens in convert_tokens_to_string by @beneyal in #15775 Bugfixes and improvements add dataset by @stevhliu in #20005 Add BERT resources by @stevhliu in #19852 Add LayoutLMv3 resource by @stevhliu in #19932 fix typo by @stevhliu in #20006 Update object detection pipeline to use post_process_object_detection methods by @alaradirik in #20004 clean up vision/text config dict arguments by @ydshieh in #19954 make sentencepiece import conditional in bertjapanesetokenizer by @ripose-jp in #20012 Fix gradient checkpoint test in encoder-decoder by @ydshieh in #20017 Quality by @sgugger in #20002 Update auto processor to check image processor created by @amyeroberts in #20021 [Doctest] Add configuration_deberta_v2.py by @Saad135 in #19995 Improve model tester by @ydshieh in #19984 Fix doctest by @ydshieh in #20023 Show installed libraries and their versions in CI jobs by @ydshieh in #20026 reorganize glossary by @stevhliu in #20010 Now supporting pathlike in pipelines too. by @Narsil in #20030 Add **kwargs by @amyeroberts in #20037 Fix some doctests after PR 15775 by @ydshieh in #20036 [Doctest] Add configuration_camembert.py by @Saad135 in #20039 [Whisper Tokenizer] Make more user-friendly by @sanchit-gandhi in #19921 [FuturWarning] Add futur warning for LEDForSequenceClassification by @ArthurZucker in #19066 fix jit trace error for model forward sequence is not aligned with jit.trace tuple input sequence, update related doc by @sywangyi in #19891 Update esmfold conversion script by @Rocketknight1 in #20028 Fixed torch.finfo issue with torch.fx by @michaelbenayoun in #20040 Only resize embeddings when necessary by @sgugger in #20043ty Speed up TF token classification postprocessing by converting complete tensors to numpy by @deutschmn in #19976 Fix ESM LM head test by @Rocketknight1 in #20045 Update README.md by @bofenghuang in #20063 fix tokenizer_type to avoid error when loading checkpoint back by @pacman100 in #20062 [Trainer] Fix model name in push_to_hub by @sanchit-gandhi in #20064 PoolformerImageProcessor defaults to match previous FE by @amyeroberts in #20048 change constant torch.tensor to torch.full by @MerHS in #20061 Update READMEs for ESMFold and add noteboo

St. Helena’s Legacy Dance Collective is one of 10 groups signed up to participate in the seventh annual Day of Dance and Cheer, hosted by the Napa High School Spiritleaders on Sunday, Dec. 11. The largest dance event in the county, with more than 500 participating last year, it will be held in Messner Gym starting at noon. Doors open at 11:30 a.m. LIVE: CHEER & DANCE STREAMING ONLINE Version 143 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.rtyrt Hollie Johnson, Napa High School dance director, created the event to showcase all of the talent in the valley and bring unity for those that all share the same passion for dance and cheer. All schools and dance studios are invited to come for free to showcase their favorite routines. Coaches also come for free and are treated to a free lunch. “We love bringing teams together,” Johnson said. “It’s my dancers’ favorite time of year. They always talk about the supportive environment and the new friends they make.” 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.tryruj 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "eetrtuj 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.ytit 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.rtyrtuj This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:yu Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2. These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 501,088 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures: Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyu Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset. Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article. Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyt Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset. We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).tyu The work is based on the following publications:tuy NCA & NDA Northeast Regional Championship 2022 live streaming online Cheer free The American Grand Grand Nationals 2022 live streaming online Cheer free Spirit Cheer Dance Grand Nationals & Cheer 2022 live streaming online Cheer free Encore Baltimore Showdown 2022 live streaming online Cheer free CHEERSPORT Oaks Classic 2022 live streaming online Cheer free Aloha Gatlinburg Showdown 2022 live streaming online Cheer free ASC Battle Under the Big Top Grand National 2022 live streaming online Cheer free Spirit Sports Worcester- National 2022 live streaming online Cheer free UDA DC Dance Challenge 2022 live streaming online Cheer free Nation's Choice Wisconsin Dells Grand National 2022 live streaming online Cheer free CHEERSPORT Greensboro State Classic 2022 live streaming online Cheer free ACP Columbus Showdown 2022 live streaming online Cheer free UCA Salt Lake City Regional 2022 live streaming online Cheer free NCA Holiday Classic 2022 live streaming online Cheer free CHEERSPORT Hot Springs Classic 2022 live streaming online Cheer free All Star Challenge Grand Nationals 2022 live streaming online Cheer free Nation’s Choice Grand Nationals 2022 live streaming online Cheer free The American Grand Nationals 2022 live streaming online Cheer free Global Events Manheim 2022 live streaming online Cheer free AAS Birmingham 2022 live streaming online Cheer free Full Out Combat Cheer Homefront Civil Showdown WA 2022 live streaming online Cheer free Celebrity Championships Branson 2022 live streaming online Cheer free Kingdom Events Manheim 2022 live streaming online Cheer free Maximum Cheer and Dance PA Madness 2022 live streaming online Cheer free World Class Cheer WCC Virtual Championship 2022 live streaming online Cheer free UCE Dayton Experience 2022 live streaming online Cheer free Cheer Derby Nashville Nationals 2022 live streaming online Cheer free Spirit Brands The Festival Wildwood 2022 live streaming online Cheer free US Cheer Productions Holiday Extravaganza Championships 2022 live streaming online Cheer free Deep South Spirit New Jersey Classic 2022 live streaming online Cheer free Gold Rush Fort Worth 2022 live streaming online Cheer free United Cheer Events Galveston Championship 2022 live streaming online Cheer free Spirit Royale Marquee Los Angeles 2022 live streaming online Cheer free MCDA Cowboy Christmas Classic West Monroe LA 2022 live streaming online Cheer free Valley of the Sun Shake Your Palm Palms 2022 live streaming online Cheer free Cheer Evolution Montreal Mayhem 2022 live streaming online Cheer free Baby I’m a Star Christmas Spectacular 2022 live streaming online Cheer free JAMZ Showdown @ The Bay 2022 live streaming online Cheer free 9 Panel Cheer All Star Jam Concord 2022 live streaming online Cheer free Bravo Spirit Christmas Classic 2022 live streaming online Cheer free COVID-19 Open Research Dataset (CORD-19). 2020. Version 2022-11-25 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2022-11-25. doi:10.5281/zenodo.3715506 Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2022-11-25) R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019 I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020) Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, and Kristina Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380 A Web user interface that uses these data to facilitate the COVID-19 literature exploration, can be found here. More details in our peer-reviewed publication here (also here there is an outdated preprint version).tuyt Funding: We acknowledge support of this work by the project "Moving from Big Data Management to Data Science" (MIS 5002437/3) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).tuyt 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.truy Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Gridtuyt (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doi All data is released in accordance with Elexon's disclaimer and reservation of rights. This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.tujty Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.tuyti The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,373,244,490 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (356,005,294 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/tuyt Wolf, Thomas; Debut, Lysandre; Sanh, Victor; Chaumond, Julien; Delangue, Clement; Moi, Anthony; Cistac, Perric; Ma, Clara; Jernite, Yacine; Plu, Julien; Xu, Canwen; Le Scao, Teven; Gugger, Sylvain; Drame, Mariama; Lhoest, Quentin; Rush, Alexander M.tut PyTorch 2.0 stack support We are very excited by the newly announced PyTorch 2.0 stack. You can enable torch.compile on any of our models, and get support with the Trainer (and in all our PyTorch examples) by using the torchdynamo training argument. For instance, just add --torchdynamo inductor when launching those examples from the command line. This API is still experimental and may be subject to changes as the PyTorch 2.0 stack matures. Note that to get the best performance, we recommend:yht using an Ampere GPU (or more recent) sticking to fixed shaped for now (so use --pad_to_max_length in our examples) Repurpose torchdynamo training args towards torch._dynamo by @sgugger in #20498 Audio Spectrogram Transformer The Audio Spectrogram Transformer model was proposed in AST: Audio Spectrogram Transformer by Yuan Gong, Yu-An Chung, James Glass. The Audio Spectrogram Transformer applies a Vision Transformer to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results for audio classification.tyuity Add Audio Spectogram Transformer by @NielsRogge in #19981 Jukebox The Jukebox model was proposed in Jukebox: A generative model for music by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditionned on an artist, genres and lyrics.tyuti Add Jukebox model (replaces #16875) by @ArthurZucker in #17826 Switch Transformers The SwitchTransformers model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity by William Fedus, Barret Zoph, Noam Shazeer. It is the first MoE model supported in transformers, with the largest checkpoint currently available currently containing 1T parameters.ytrtuj Add Switch transformers by @younesbelkada and @ArthurZucker in #19323 RocBert The RoCBert model was proposed in RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou. It's a pretrained Chinese language model that is robust under various forms of adversarial attacks.tyut Add RocBert by @sww9370 in #20013 CLIPSeg The CLIPSeg model was proposed in Image Segmentation Using Text and Image Prompts by Timo Lüddecke and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen CLIP model for zero- and one-shot image segmentation.rytru NAT was proposed in Neighborhood Attention Transformer by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.tyht It is a hierarchical vision transformer based on Neighborhood Attention, a sliding-window self attention pattern. DiNAT DiNAT was proposed in Dilated Neighborhood Attention Transformer by Ali Hassani and Humphrey Shi. It extends NAT by adding a Dilated Neighborhood Attention pattern to capture global context, and shows significant performance improvements over it.rytu Add Neighborhood Attention Transformer (NAT) and Dilated NAT (DiNAT) models by @alihassanijr in #20219 MobileNetV2 The MobileNet model was proposed in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.tryrtuj add MobileNetV2 model by @hollance in #17845 MobileNetV1 The MobileNet model was proposed in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.tyhu add MobileNetV1 model by @hollance in #17799 Image processors Image processors replace feature extractors as the processing class for computer vision models.rtyhtu Important changes: size parameter is now a dictionary of {"height": h, "width": w}, {"shortest_edge": s}, {"shortest_egde": s, "longest_edge": l} instead of int or tuple. Addition of data_format flag. You can now specify if you want your images to be returned in "channels_first" - NCHW - or "channels_last" - NHWC - format. Processing flags e.g. do_resize can be passed directly to the preprocess method instead of modifying the class attribute: image_processor([image_1, image_2], do_resize=False, return_tensors="pt", data_format="channels_last") Leaving return_tensors unset will return a list of numpy arrays. The classes are backwards compatible and can be created using existing feature extractor configurations - with the size parameter converted.tyr Add Image Processors by @amyeroberts in #19796 Add Donut image processor by @amyeroberts #20425 Add segmentation + object detection image processors by @amyeroberts in #20160 AutoImageProcessor by @amyeroberts in #20111 Backbone for computer vision models

The last PRO race of the 2022 season and the honours at IRONMAN 70.3 New Zealand went to local favourite Jack Moody in the men’s race and the sole European challenger in the women’s, Sweden’s Anna Bergsten. LIVE: TRIATHLON 2022 STREAMING ONLINE Version 143 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.rtyrt he swim saw three men lead the way – Aussie Charlie Quin, Benjamin Zorgnotti (TAH) and Sam Osborne (AUS). Moody, wearing the #1 bib was 48 seconds behind in fourth but the Aucklander would move into a share of the lead midway through the bike alongside Quin. Quin was the form athlete after a dream run since moving up to middle distance racing – a win at the Noosa Triathlon followed by a second-place finish at last month’s 70.3 Melbourne and a victory at the Laguna Phuket Triathlon in Thailand. But by the end of the bike leg it was Moody who had taken command. He too has had a strong season, featuring a third at IRONMAN Australia in May and a second at IRONMAN 70.3 Oregon in August. Coming out of T2 he was nearly four minutes clear of his rivals. 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.tryruj 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "eetrtuj 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.ytit 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.rtyrtuj This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:yu Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2. These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 501,088 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures: Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyu Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset. Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article. Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyt Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset. We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).tyu The work is based on the following publications:tuy COVID-19 Open Research Dataset (CORD-19). 2020. Version 2022-11-25 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2022-11-25. doi:10.5281/zenodo.3715506 Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2022-11-25) R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019 I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020) Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, and Kristina Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380 A Web user interface that uses these data to facilitate the COVID-19 literature exploration, can be found here. More details in our peer-reviewed publication here (also here there is an outdated preprint version).tuyt Funding: We acknowledge support of this work by the project "Moving from Big Data Management to Data Science" (MIS 5002437/3) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).tuyt 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.truy Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Gridtuyt (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doi All data is released in accordance with Elexon's disclaimer and reservation of rights. This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.tujty Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.tuyti The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,373,244,490 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (356,005,294 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/tuyt Wolf, Thomas; Debut, Lysandre; Sanh, Victor; Chaumond, Julien; Delangue, Clement; Moi, Anthony; Cistac, Perric; Ma, Clara; Jernite, Yacine; Plu, Julien; Xu, Canwen; Le Scao, Teven; Gugger, Sylvain; Drame, Mariama; Lhoest, Quentin; Rush, Alexander M.tut PyTorch 2.0 stack support We are very excited by the newly announced PyTorch 2.0 stack. You can enable torch.compile on any of our models, and get support with the Trainer (and in all our PyTorch examples) by using the torchdynamo training argument. For instance, just add --torchdynamo inductor when launching those examples from the command line. This API is still experimental and may be subject to changes as the PyTorch 2.0 stack matures. Note that to get the best performance, we recommend:yht using an Ampere GPU (or more recent) sticking to fixed shaped for now (so use --pad_to_max_length in our examples) Repurpose torchdynamo training args towards torch._dynamo by @sgugger in #20498 Audio Spectrogram Transformer The Audio Spectrogram Transformer model was proposed in AST: Audio Spectrogram Transformer by Yuan Gong, Yu-An Chung, James Glass. The Audio Spectrogram Transformer applies a Vision Transformer to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results for audio classification.tyuity Add Audio Spectogram Transformer by @NielsRogge in #19981 Jukebox The Jukebox model was proposed in Jukebox: A generative model for music by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditionned on an artist, genres and lyrics.tyuti Add Jukebox model (replaces #16875) by @ArthurZucker in #17826 Switch Transformers The SwitchTransformers model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity by William Fedus, Barret Zoph, Noam Shazeer. It is the first MoE model supported in transformers, with the largest checkpoint currently available currently containing 1T parameters.ytrtuj Add Switch transformers by @younesbelkada and @ArthurZucker in #19323 RocBert The RoCBert model was proposed in RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou. It's a pretrained Chinese language model that is robust under various forms of adversarial attacks.tyut Add RocBert by @sww9370 in #20013 CLIPSeg The CLIPSeg model was proposed in Image Segmentation Using Text and Image Prompts by Timo Lüddecke and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen CLIP model for zero- and one-shot image segmentation.rytru NAT was proposed in Neighborhood Attention Transformer by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.tyht It is a hierarchical vision transformer based on Neighborhood Attention, a sliding-window self attention pattern. DiNAT DiNAT was proposed in Dilated Neighborhood Attention Transformer by Ali Hassani and Humphrey Shi. It extends NAT by adding a Dilated Neighborhood Attention pattern to capture global context, and shows significant performance improvements over it.rytu Add Neighborhood Attention Transformer (NAT) and Dilated NAT (DiNAT) models by @alihassanijr in #20219 MobileNetV2 The MobileNet model was proposed in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.tryrtuj add MobileNetV2 model by @hollance in #17845 MobileNetV1 The MobileNet model was proposed in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.tyhu add MobileNetV1 model by @hollance in #17799 Image processors Image processors replace feature extractors as the processing class for computer vision models.rtyhtu Important changes: size parameter is now a dictionary of {"height": h, "width": w}, {"shortest_edge": s}, {"shortest_egde": s, "longest_edge": l} instead of int or tuple. Addition of data_format flag. You can now specify if you want your images to be returned in "channels_first" - NCHW - or "channels_last" - NHWC - format. Processing flags e.g. do_resize can be passed directly to the preprocess method instead of modifying the class attribute: image_processor([image_1, image_2], do_resize=False, return_tensors="pt", data_format="channels_last") Leaving return_tensors unset will return a list of numpy arrays. The classes are backwards compatible and can be created using existing feature extractor configurations - with the size parameter converted.tyr Add Image Processors by @amyeroberts in #19796 Add Donut image processor by @amyeroberts #20425 Add segmentation + object detection image processors by @amyeroberts in #20160 AutoImageProcessor by @amyeroberts in #20111 Backbone for computer vision models We're adding support for a general AutoBackbone class, which turns any vision model (like ConvNeXt, Swin Transformer) into a backbone to be used with frameworks like DETR and Mask R-CNN. The design is in early stages and we welcome feedback.tyu Add AutoBackbone + ResNetBackbone by @NielsRogge in #20229 Improve backbone by @NielsRogge in #20380 [AutoBackbone] Improve API by @NielsRogge in #20407 Support for safetensors offloading If the model you are using has a safetensors checkpoint and you have the library installed, offload to disk will take advantage of this to be more memory efficient and roughly 33% faster.dyhrtju Safetensors offload by @sgugger in #20321 Contrastive search in the generate method Generate: TF contrastive search with XLA support by @gante in #20050 Generate: contrastive search with full optional outputs by @gante in #19963 Breaking changes 🚨 🚨 🚨 Fix Issue 15003: SentencePiece Tokenizers Not Adding Special Tokens in convert_tokens_to_string by @beneyal in #15775 Bugfixes and improvements add dataset by @stevhliu in #20005 Add BERT resources by @stevhliu in #19852 Add LayoutLMv3 resource by @stevhliu in #19932 fix typo by @stevhliu in #20006 Update object detection pipeline to use post_process_object_detection methods by @alaradirik in #20004 clean up vision/text config dict arguments by @ydshieh in #19954 make sentencepiece import conditional in bertjapanesetokenizer by @ripose-jp in #20012 Fix gradient checkpoint test in encoder-decoder by @ydshieh in #20017 Quality by @sgugger in #20002 Update auto processor to check image processor created by @amyeroberts in #20021 [Doctest] Add configuration_deberta_v2.py by @Saad135 in #19995 Improve model tester by @ydshieh in #19984 Fix doctest by @ydshieh in #20023 Show installed libraries and their versions in CI jobs by @ydshieh in #20026 reorganize glossary by @stevhliu in #20010 Now supporting pathlike in pipelines too. by @Narsil in #20030 Add **kwargs by @amyeroberts in #20037 Fix some doctests after PR 15775 by @ydshieh in #20036 [Doctest] Add configuration_camembert.py by @Saad135 in #20039 [Whisper Tokenizer] Make more user-friendly by @sanchit-gandhi in #19921 [FuturWarning] Add futur warning for LEDForSequenceClassification by @ArthurZucker in #19066 fix jit trace error for model forward sequence is not aligned with jit.trace tuple input sequence, update related doc by @sywangyi in #19891 Update esmfold conversion script by @Rocketknight1 in #20028 Fixed torch.finfo issue with torch.fx by @michaelbenayoun in #20040 Only resize embeddings when necessary by @sgugger in #20043ty Speed up TF token classification postprocessing by converting complete tensors to numpy by @deutschmn in #19976 Fix ESM LM head test b

Freezing weather conditions have forced a change in venue for tomorrow evening’s Munster club senior football championship final between Kerins O’Rahillys and Newcastle West. The Kerry champions were set to meet their Limerick counterparts at Pairc Ui Rinn at 7.30pm but Munster Council has changed the decider’s venue to Mallow instead. LIVE: GAA FOOTBALL 2022 STREAMING ONLINE Version 143 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.rtyrt Along with the venue change, the match is also now set to throw-in at 3pm. The switch to mallow also leaves uncertainty over the planned television coverage of the game, as TG4 had planned to show the match live tomorrow evening but are already scheduled to air the All-Ireland ladies intermediate club football championship final live from Croke Park at 3pm. 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.tryruj 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "eetrtuj 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.ytit 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.rtyrtuj This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:yu Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2. These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 501,088 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures: Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyu Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset. Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article. Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyt Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset. We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).tyu The work is based on the following publications:tuy COVID-19 Open Research Dataset (CORD-19). 2020. Version 2022-11-25 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2022-11-25. doi:10.5281/zenodo.3715506 Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2022-11-25) R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019 I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020) Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, and Kristina Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380 A Web user interface that uses these data to facilitate the COVID-19 literature exploration, can be found here. More details in our peer-reviewed publication here (also here there is an outdated preprint version).tuyt Funding: We acknowledge support of this work by the project "Moving from Big Data Management to Data Science" (MIS 5002437/3) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).tuyt 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.truy Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Gridtuyt (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doi All data is released in accordance with Elexon's disclaimer and reservation of rights. This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.tujty Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.tuyti The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,373,244,490 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (356,005,294 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/tuyt Wolf, Thomas; Debut, Lysandre; Sanh, Victor; Chaumond, Julien; Delangue, Clement; Moi, Anthony; Cistac, Perric; Ma, Clara; Jernite, Yacine; Plu, Julien; Xu, Canwen; Le Scao, Teven; Gugger, Sylvain; Drame, Mariama; Lhoest, Quentin; Rush, Alexander M.tut PyTorch 2.0 stack support We are very excited by the newly announced PyTorch 2.0 stack. You can enable torch.compile on any of our models, and get support with the Trainer (and in all our PyTorch examples) by using the torchdynamo training argument. For instance, just add --torchdynamo inductor when launching those examples from the command line. This API is still experimental and may be subject to changes as the PyTorch 2.0 stack matures. Note that to get the best performance, we recommend:yht using an Ampere GPU (or more recent) sticking to fixed shaped for now (so use --pad_to_max_length in our examples) Repurpose torchdynamo training args towards torch._dynamo by @sgugger in #20498 Audio Spectrogram Transformer The Audio Spectrogram Transformer model was proposed in AST: Audio Spectrogram Transformer by Yuan Gong, Yu-An Chung, James Glass. The Audio Spectrogram Transformer applies a Vision Transformer to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results for audio classification.tyuity Add Audio Spectogram Transformer by @NielsRogge in #19981 Jukebox The Jukebox model was proposed in Jukebox: A generative model for music by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditionned on an artist, genres and lyrics.tyuti Add Jukebox model (replaces #16875) by @ArthurZucker in #17826 Switch Transformers The SwitchTransformers model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity by William Fedus, Barret Zoph, Noam Shazeer. It is the first MoE model supported in transformers, with the largest checkpoint currently available currently containing 1T parameters.ytrtuj Add Switch transformers by @younesbelkada and @ArthurZucker in #19323 RocBert The RoCBert model was proposed in RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou. It's a pretrained Chinese language model that is robust under various forms of adversarial attacks.tyut Add RocBert by @sww9370 in #20013 CLIPSeg The CLIPSeg model was proposed in Image Segmentation Using Text and Image Prompts by Timo Lüddecke and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen CLIP model for zero- and one-shot image segmentation.rytru NAT was proposed in Neighborhood Attention Transformer by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.tyht It is a hierarchical vision transformer based on Neighborhood Attention, a sliding-window self attention pattern. DiNAT DiNAT was proposed in Dilated Neighborhood Attention Transformer by Ali Hassani and Humphrey Shi. It extends NAT by adding a Dilated Neighborhood Attention pattern to capture global context, and shows significant performance improvements over it.rytu Add Neighborhood Attention Transformer (NAT) and Dilated NAT (DiNAT) models by @alihassanijr in #20219 MobileNetV2 The MobileNet model was proposed in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.tryrtuj add MobileNetV2 model by @hollance in #17845 MobileNetV1 The MobileNet model was proposed in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.tyhu add MobileNetV1 model by @hollance in #17799 Image processors Image processors replace feature extractors as the processing class for computer vision models.rtyhtu Important changes: size parameter is now a dictionary of {"height": h, "width": w}, {"shortest_edge": s}, {"shortest_egde": s, "longest_edge": l} instead of int or tuple. Addition of data_format flag. You can now specify if you want your images to be returned in "channels_first" - NCHW - or "channels_last" - NHWC - format. Processing flags e.g. do_resize can be passed directly to the preprocess method instead of modifying the class attribute: image_processor([image_1, image_2], do_resize=False, return_tensors="pt", data_format="channels_last") Leaving return_tensors unset will return a list of numpy arrays. The classes are backwards compatible and can be created using existing feature extractor configurations - with the size parameter converted.tyr Add Image Processors by @amyeroberts in #19796 Add Donut image processor by @amyeroberts #20425 Add segmentation + object detection image processors by @amyeroberts in #20160 AutoImageProcessor by @amyeroberts in #20111 Backbone for computer vision models We're adding support for a general AutoBackbone class, which turns any vision model (like ConvNeXt, Swin Transformer) into a backbone to be used with frameworks like DETR and Mask R-CNN. The design is in early stages and we welcome feedback.tyu Add AutoBackbone + ResNetBackbone by @NielsRogge in #20229 Improve backbone by @NielsRogge in #20380 [AutoBackbone] Improve API by @NielsRogge in #20407 Support for safetensors offloading If the model you are using has a safetensors checkpoint and you have the library installed, offload to disk will take advantage of this to be more memory efficient and roughly 33% faster.dyhrtju Safetensors offload by @sgugger in #20321 Contrastive search in the generate method Generate: TF contrastive search with XLA support by @gante in #20050 Generate: contrastive search with full optional outputs by @gante in #19963 Breaking changes 🚨 🚨 🚨 Fix Issue 15003: SentencePiece Tokenizers Not Adding Special Tokens in convert_tokens_to_string by @beneyal in #15775 Bugfixes and improvements add dataset by @stevhliu in #20005 Add BERT resources by @stevhliu in #19852 Add LayoutLMv3 resource by @stevhliu in #19932 fix typo by @stevhliu in #20006 Update object detection pipeline to use post_process_object_detection methods by @alaradirik in #20004 clean up vision/text config dict arguments by @ydshieh in #19954 make sentencepiece import conditional in bertjapanesetokenizer by @ripose-jp in #20012 Fix gradient checkpoint test in encoder-decoder by @ydshieh in #20017 Quality by @sgugger in #20002 Update auto processor to check image processor created by @amyeroberts in #20021 [Doctest] Add configuration_deberta_v2.py by @Saad135 in #19995 Improve model tester by @ydshieh in #19984 Fix doctest by @ydshieh in #20023 Show installed libraries and their versions in CI jobs by @ydshieh in #20026 reorganize glossary by @stevhliu in #20010 Now supporting pathlike in pipelines too. by @Narsil in #20030 Add **kwargs by @amyeroberts in #20037 Fix some doctests after PR 15775 by @ydshieh in #20036 [Doctest] Add configuration_camembert.py by @Saad135 in #20039 [Whisper Tokenizer] Make more user-friendly by @sanchit-gandhi in #19921 [FuturWarning] Add futur warning for LEDForSequenceClassification by @ArthurZucker in #19066 fix jit trace error for model forward sequence is not aligned with jit.trace tuple input sequence, update related doc by @sywangyi in #19891 Update esmfold conversion script by @Rocketknight1 in #20028 Fixed torch.finfo issue with torch.fx by @michaelbenayoun in #20040 Only resize embeddings when necessary by @sgugger in #20043ty Speed up TF token classification postprocessing by converting complete tensors to numpy by @deutschmn in #19976 Fix ESM LM head test by @Rocketknight1 in #20045 Update README.md by @bofenghuang in #20063 fix tokenizer_type to avoid error when loading checkpoint back by @pacman100 in #20062 [Trainer] Fix model name in push_to_hub by @sanchit-gandhi in #20064 PoolformerImageProcessor defaults to match previous FE by @amyeroberts in #20048 cha

The honour of becoming the first teams to line out in an All-Ireland Ladies club football final at Croke Park will fall to Longford Slashers and their opponents from Tipperary, Mullinahone. LIVE: GAA FOOTBALL 2022 STREAMING ONLINE Version 143 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.rtyrt Slashers will become the very first team from Longford to contest an All-Ireland Ladies club football decider. Mullinahone making an impressive step-up from the junior ranks to reach Saturday’s showpiece. It was just last February when Mullinahone appeared in an All-Ireland junior decider. Unfortunately from their point of view, they came up short against Dublin opponents St Judes. 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.tryruj 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "eetrtuj 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.ytit 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.rtyrtuj This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:yu Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2. These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 501,088 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures: Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyu Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset. Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article. Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyt Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset. We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).tyu The work is based on the following publications:tuy COVID-19 Open Research Dataset (CORD-19). 2020. Version 2022-11-25 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2022-11-25. doi:10.5281/zenodo.3715506 Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2022-11-25) R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019 I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020) Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, and Kristina Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380 A Web user interface that uses these data to facilitate the COVID-19 literature exploration, can be found here. More details in our peer-reviewed publication here (also here there is an outdated preprint version).tuyt Funding: We acknowledge support of this work by the project "Moving from Big Data Management to Data Science" (MIS 5002437/3) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).tuyt 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.truy Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Gridtuyt (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doi All data is released in accordance with Elexon's disclaimer and reservation of rights. This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.tujty Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.tuyti The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,373,244,490 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (356,005,294 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/tuyt Wolf, Thomas; Debut, Lysandre; Sanh, Victor; Chaumond, Julien; Delangue, Clement; Moi, Anthony; Cistac, Perric; Ma, Clara; Jernite, Yacine; Plu, Julien; Xu, Canwen; Le Scao, Teven; Gugger, Sylvain; Drame, Mariama; Lhoest, Quentin; Rush, Alexander M.tut PyTorch 2.0 stack support We are very excited by the newly announced PyTorch 2.0 stack. You can enable torch.compile on any of our models, and get support with the Trainer (and in all our PyTorch examples) by using the torchdynamo training argument. For instance, just add --torchdynamo inductor when launching those examples from the command line. This API is still experimental and may be subject to changes as the PyTorch 2.0 stack matures. Note that to get the best performance, we recommend:yht using an Ampere GPU (or more recent) sticking to fixed shaped for now (so use --pad_to_max_length in our examples) Repurpose torchdynamo training args towards torch._dynamo by @sgugger in #20498 Audio Spectrogram Transformer The Audio Spectrogram Transformer model was proposed in AST: Audio Spectrogram Transformer by Yuan Gong, Yu-An Chung, James Glass. The Audio Spectrogram Transformer applies a Vision Transformer to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results for audio classification.tyuity Add Audio Spectogram Transformer by @NielsRogge in #19981 Jukebox The Jukebox model was proposed in Jukebox: A generative model for music by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditionned on an artist, genres and lyrics.tyuti Add Jukebox model (replaces #16875) by @ArthurZucker in #17826 Switch Transformers The SwitchTransformers model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity by William Fedus, Barret Zoph, Noam Shazeer. It is the first MoE model supported in transformers, with the largest checkpoint currently available currently containing 1T parameters.ytrtuj Add Switch transformers by @younesbelkada and @ArthurZucker in #19323 RocBert The RoCBert model was proposed in RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou. It's a pretrained Chinese language model that is robust under various forms of adversarial attacks.tyut Add RocBert by @sww9370 in #20013 CLIPSeg The CLIPSeg model was proposed in Image Segmentation Using Text and Image Prompts by Timo Lüddecke and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen CLIP model for zero- and one-shot image segmentation.rytru NAT was proposed in Neighborhood Attention Transformer by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.tyht It is a hierarchical vision transformer based on Neighborhood Attention, a sliding-window self attention pattern. DiNAT DiNAT was proposed in Dilated Neighborhood Attention Transformer by Ali Hassani and Humphrey Shi. It extends NAT by adding a Dilated Neighborhood Attention pattern to capture global context, and shows significant performance improvements over it.rytu Add Neighborhood Attention Transformer (NAT) and Dilated NAT (DiNAT) models by @alihassanijr in #20219 MobileNetV2 The MobileNet model was proposed in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.tryrtuj add MobileNetV2 model by @hollance in #17845 MobileNetV1 The MobileNet model was proposed in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.tyhu add MobileNetV1 model by @hollance in #17799 Image processors Image processors replace feature extractors as the processing class for computer vision models.rtyhtu Important changes: size parameter is now a dictionary of {"height": h, "width": w}, {"shortest_edge": s}, {"shortest_egde": s, "longest_edge": l} instead of int or tuple. Addition of data_format flag. You can now specify if you want your images to be returned in "channels_first" - NCHW - or "channels_last" - NHWC - format. Processing flags e.g. do_resize can be passed directly to the preprocess method instead of modifying the class attribute: image_processor([image_1, image_2], do_resize=False, return_tensors="pt", data_format="channels_last") Leaving return_tensors unset will return a list of numpy arrays. The classes are backwards compatible and can be created using existing feature extractor configurations - with the size parameter converted.tyr Add Image Processors by @amyeroberts in #19796 Add Donut image processor by @amyeroberts #20425 Add segmentation + object detection image processors by @amyeroberts in #20160 AutoImageProcessor by @amyeroberts in #20111 Backbone for computer vision models We're adding support for a general AutoBackbone class, which turns any vision model (like ConvNeXt, Swin Transformer) into a backbone to be used with frameworks like DETR and Mask R-CNN. The design is in early stages and we welcome feedback.tyu Add AutoBackbone + ResNetBackbone by @NielsRogge in #20229 Improve backbone by @NielsRogge in #20380 [AutoBackbone] Improve API by @NielsRogge in #20407 Support for safetensors offloading If the model you are using has a safetensors checkpoint and you have the library installed, offload to disk will take advantage of this to be more memory efficient and roughly 33% faster.dyhrtju Safetensors offload by @sgugger in #20321 Contrastive search in the generate method Generate: TF contrastive search with XLA support by @gante in #20050 Generate: contrastive search with full optional outputs by @gante in #19963 Breaking changes 🚨 🚨 🚨 Fix Issue 15003: SentencePiece Tokenizers Not Adding Special Tokens in convert_tokens_to_string by @beneyal in #15775 Bugfixes and improvements add dataset by @stevhliu in #20005 Add BERT resources by @stevhliu in #19852 Add LayoutLMv3 resource by @stevhliu in #19932 fix typo by @stevhliu in #20006 Update object detection pipeline to use post_process_object_detection methods by @alaradirik in #20004 clean up vision/text config dict arguments by @ydshieh in #19954 make sentencepiece import conditional in bertjapanesetokenizer by @ripose-jp in #20012 Fix gradient checkpoint test in encoder-decoder by @ydshieh in #20017 Quality by @sgugger in #20002 Update auto processor to check image processor created by @amyeroberts in #20021 [Doctest] Add configuration_deberta_v2.py by @Saad135 in #19995 Improve model tester by @ydshieh in #19984 Fix doctest by @ydshieh in #20023 Show installed libraries and their versions in CI jobs by @ydshieh in #20026 reorganize glossary by @stevhliu in #20010 Now supporting pathlike in pipelines too. by @Narsil in #20030 Add **kwargs by @amyeroberts in #20037 Fix some doctests after PR 15775 by @ydshieh in #20036 [Doctest] Add configuration_camembert.py by @Saad135 in #20039 [Whisper Tokenizer] Make more user-friendly by @sanchit-gandhi in #19921 [FuturWarning] Add futur warning for LEDForSequenceClassification by @ArthurZucker in #19066 fix jit trace error for model forward sequence is not aligned with jit.trace tuple input sequence, update related doc by @sywangyi in #19891 Update esmfold conversion script by @Rocketknight1 in #20028 Fixed torch.finfo issue with torch.fx by @michaelbenayoun in #20040 Only resize embeddings when necessary by @sgugger in #20043ty Speed up TF token classification postprocessing by converting complete tensors to numpy by @deutschmn in #19976 Fix ESM LM head test by @Rocketknight1 in #20045 Update README.md by @bofenghuang in #20063 fix tokenizer_type to avoid error when loading checkpoint back by @pacman100 in #20062 [Trainer] Fix model name in push_to_hub by @sanchit-gandhi in #20064 PoolformerImageProcessor defaults to match previous FE by @amyeroberts in #20048 change constant torch.tensor to torch.full by @MerHS in #20061 Update READMEs for ESMFold and add notebooks by @Rocketknight1

Ruiz at Budo Sento Championship 12 on Tapology. View Zamora vs. Ruiz fight video, highlights, news, Twitter updates, and fight results. WATCH LIVE STREAMS HERE Recent studies show a correlation between the content of vitamin D3 in the human body and the severity of COVID19. Part of the world’s population is deficient in vitamin D3. How to watch Bellator 289: Stots vs Sabatello MMA fans can watch Bellator 289: Stots vs Sabatello live stream on Showtime in the United States. The date is Friday, December 9. The start time is scheduled for 9 pm ET / 6 pm PT. Bellator 289 preliminary card begins at 5 pm ET / 2 pm PT live stream on YouTube. Fans in the countries with no local coverage can connect via VPN, such as ExpressVPN, and live stream Bellator 289: Stots vs Sabatello from practically anywhere. Bellator 289 fight card The current Bellator 289: Stots vs Sabatello fight card looks as the following: Main Card Raufeon Stots vs. Danny Sabatello – Stots’s interim Bellator bantamweight title, bantamweight Grand Prix semi-final Liz Carmouche vs. Juliana Velasquez – Carmouche’s Bellator flyweight title Patchy Mix vs. Magomed Magomedov – Bellator bantamweight Grand Prix semi-final Dalton Rosta vs. Anthony Adams Preliminary Card Denise Kielholtz vs. Ilara Joanne Cody Law vs. Cris Lencioni Kyle Crutchmer vs. Jaleel Willis Kai Kamaka vs. Kevin Boehm Mark Lemminger vs. Michael Lombardo Pat Downey vs. Christian Echols Cass Bell vs. Jared Scoggins The solution to this problem is possible by the development and inclusion of foodstuffs fortified with vitamin D in diets. The aim of this study was to develop a D3 -fortified sour cream dessert using an emulsion system as a vitamin D delivery system. Commercially available raw materials: vitamin D3 powder, sodium carboxymethylcellulose, skimmed milk powder, and sunflower oil were used to create a vitamin D-fortified emulsion.yuiysdg The latter is used in the technology of sour cream dessert production. The emulsion microstructure and stability were investigated using rheology and dynamic light scattering methods. The content of vitamin D3 was determined by coulometric titration and spectroscopy. Experimentally determined data on the viscosity of emulsions indicate the pseudoplastic behavior of the f low. The use of a structural approach (Casson model) made it possible to determine the emulsion viscosity parameters, which can be used as a quantitative criterion for emulsion stability.ujtyk This conclusion was confirmed by microstructural data on distribution size of droplets volume of emulsion. Amount of vitamin D in the emulsion and dessert was 1.96 ± 0.22 µg/g (97.8 % of the added amount) and 0.019±0,005 µg/g, respectively. Using the developed stable emulsion as a vitamin D delivery system, a technology for the production of a dessert based on sour cream fortified with vitamin D3 was proposed.ytujty The Worldwide Soundscapes project is a global, open inventory of spatio-temporally replicated soundscape datasets. This Zenodo entry comprises the data tables that constitute its (meta-)database, as well as their description.yuy The overview of all sampling sites can be found on the corresponding project on ecoSound-web, as well as a demonstration collection containing selected recordings. More information on the project can be found here and on ResearchGate.yuji The audio recording criteria justifying inclusion into the meta-database are: Stationary (no transects, towed sensors or microphones mounted on cars) Passive (unattended, no human disturbance by the recordist) Ambient (no spatial or temporal focus on a particular species or direction) Spatially and/or temporally replicated (multiple sites sampled at least at one common daytime or multiple days sampled at least in one common site)tyuyt The individual columns of the provided data tables are described in the following. Data tables are linked through primary keys; joining them will result in a database.ytuj datasets dataset_id: incremental integer, primary key name: name of the dataset. if it is repeated, incremental integers should be used in the "subset" column to differentiate them. subset: incremental integer that can be used to distinguish datasets with identical names collaborators: full names of people deemed responsible for the dataset, separated by commas contributors: full names of people who are not the main collaborators but who have significantly contributed to the dataset, and who could be contacted for in-depth analyses, separated by commas. date_added: when the datased was added (DD/MM/YYYY) URL_open_recordings: if recordings (even only some) from this dataset are openly available, indicate the internet link where they can be found. URL_project: internet link for further information about the corresponding project DOI_publication: DOI of corresponding publications, separated by comma core_realm_IUCN: The core realm of the dataset. Datasets may have multiple realms, but the main one should be listed. Datasets may contain sampling sites from different realms in the "sites" sheet. IUCN Global Ecosystem Typology (v2.0): https://global-ecosystems.org/ medium: the physical medium the microphone is situated in protected_area: Whether the sampling sites were situated in protected areas or not, or only some. GADM0: For datasets on land or in territorial waters, Global Administrative Database level0 https://gadm.org/ GADM1: For datasets on land or in territorial waters, Global Administrative Database level1 https://gadm.org/ GADM2: For datasets on land or in territorial waters, Global Administrative Database level2 https://gadm.org/ IHO: For marine locations, the sea area that encompassess all the sampling locations according to the International Hydrographic Organisation. Map here: https://www.arcgis.com/home/item.html?id=44e04407fbaf4d93afcb63018fbca9e2 locality: optional free text about the locality latitude_numeric_region: study region approximate centroid latitude in WGS84 decimal degrees longitude_numeric_region: study region approximate centroid longitude in WGS84 decimal degrees sites_number: number of sites sampled year_start: starting year of the sampling year_end: ending year of the sampling deployment_schedule: description of the sampling schedule, provisional temporal_recording_selection: list environmental exclusion criteria that were used to determine which recording days or times to discard high_pass_filter_Hz: frequency of the high-pass filter of the recorder, in Hz variable_sampling_frequency: Does the sampling frequency vary? If it does, write "NA" in the sampling_frequency_kHz column and indicate it in the sampling_frequency_kHz column inside the deployments sheet sampling_frequency_kHz: frequency the microphone was sampled at (sounds of half that frequency will be recorded) variable_recorder: recorder: recorder model used microphone: microphone used freshwater_recordist_position: position of the recordist relative to the microphone during sampling (only for freshwater) collaborator_comments: free-text field for comments by the collaborators validated: This cell is checked if the contents of all sheets are complete and have been found to be coherent and consistent with our requirements. validator_name: name of person doing the validation validation_comments: validators: please insert the date when someone was contacted cross-check: this cell is checked if the collaborators confirm the spatial and temporal data after checking the corresponding site maps, deployment and operation time graphs found at https://drive.google.com/drive/folders/1qfwXH_7dpFCqyls-c6b8RZ_fbcn9kXbp?usp=share_linktuy datasets-sites dataset_ID: primary key of datasets table dataset_name: lookup field site_ID: primary key of sites table site_name: lookup field sites site_ID: unique site IDs, larger than 1000 for compatibility with ecoSound-web site_name: name or code of sampling site as used in respective projects latitude_numeric: exact numeric degrees coordinates of latitude longitude_numeric: exact numeric degrees coordinates of longitude topography_m: for sites on land: elevation. For marine sites: depth (negative). in meters freshwater_depth_m realm: Ecosystem type according to IUCN GET https://global-ecosystems.org/ biome: Ecosystem type according to IUCN GET https://global-ecosystems.org/ functional_group: Ecosystem type according to IUCN GET https://global-ecosystems.org/ commentstuyt deployments dataset_ID: primary key of datasets table dataset_name: lookup field deployment: use identical subscript letters to denote rows that belong to the same deployment. For instance, you may use different operation times and schedules for different target taxa within one deployment. start_date_min: earliest date of deployment start, double-click cell to get date-picker start_date_max: latest date of deployment start, if applicable (only used when recorders were deployed over several days), double-click cell to get date-picker start_time_mixed: deployment start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Corresponds to the recording start time for continuous recording deployments. If multiple start times were used, you should mention the latest start time (corresponds to the earliest daytime from which all recorders are active). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60") permanent: is the deployment permanent (in which case it would be ongoing and the end date or duration would be unknown)? variable_duration_days: is the duration of the deployment variable? in days duration_days: deployment duration per recorder (use the minimum if variable) end_date_min: earliest date of deployment end, only needed if duration is variable, double-click cell to get date-picker end_date_max: latest date of deployment end, only needed if duration is variable, double-click cell to get date-pickertuy end_time_mixed: deployment end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Corresponds to the recording end time for continuous recording deployments. recording_time: does the recording last from the deployment start time to the end time (continuous) or at scheduled daily intervals (scheduled)? Note: we consider recordings with duty cycles to be continuous. operation_start_time_mixed: scheduled recording start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60") operation_duration_minutes: duration of operation in minutes, if constant operation_end_time_mixed: scheduled recording end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60") duty_cycle_minutes: duty cycle of the recording (i.e. the fraction of minutes when it is recording), written as "recording(minutes)/period(minutes)". For example: "1/6" if the recorder is active for 1 minute and standing by for 5 minutes. sampling_frequency_kHz: only indicate the sampling frequency if it is variable within a particular dataset so that we need to code different frequencies for different deployments recorder subset_sites: If the deployment was not done in all the sites of the corresponding datasest, site IDs can be indicated here, separated by commas comments We investigated the influence of wormwood-wild rue mixture with high anthelmintic effect on the diuretic process in sheep and on the physical and chemical properties of the urinary excretion of the sheep fed with (6 g / kg), three and fivefold increased therapeutic dose (18 and 30 g / kg) of the mixture. No pain was observed during urination in the experimental animals. The urine of the experimental animals was clear, light yellowish in color, there was no smell. The density of urine in animals fed with the mixture at a dose of 30 g / kg was 1.029, pH was 8.48, which is the norm. Proteins, sugars, ketone bodies, bilirubin were not found in the urine of animals undergoing experiments. In the tested urine, individual blood vessels appeared, and a small amount of indican and urobilins was found. The findings show that wormwood does not have a toxic effect on the physical and chemical properties of urine in sheep.tu is an open-source package which allows to focus on a network-oriented approach to identify regulatory mechanisms linked to a disease, identify genes of interest, simulate and score the effect of a drug at transcriptional level, and perform drug repurposing with adaptive testing.tu The article informs about the ecological evaluation of soils in the Kangarli administrative Region. For the ecological evaluation of soils, physico-geographical condition of this area (relief, climate, hydrological and hydrogeological, plant and animal world, anthropogenic influence, etc.) degradation processes (salinity, erosion, waterlogging, rockiness, overgrown areas, etc) morphological, physical and chemical characteristics in the region were studied. At the same time, soils under cultivated and natural plants were assessed. The highest points received mountain chestnut (brown) (100 points), chestnut (brown) (96 points), alluvial (92 points) soils. The lowest points received sandy marshy-meadow (32 points), stony-gravelly river bed (18 points) and stony river bed (10 points) soils. Some recommendations and suggestions for the rational use of the soils for the cultural and natural plants of the Kengirlinsky administrative region were made.thtyj This dataset contains a selection of bias-corrected data from the preoperational MiKlip system for decadal climate predictions (Mueller et al., 2018) used within the Italian research project PNRA18_00199-IPSODES. The adopted method for bias correction is described in the file bias_correction.pdf. Also data from the assimilation run are provided. Nomenclature of variables follows that of the original MiKlip output.tyuht Mueller, W., et al. A Higher‐resolution Version of the Max Planck Institute Earth System Model (MPI‐ESM1.2‐HR). J. Adv. Model. Earth Syst. 10, 1383-1413 (2018)tru

Cheetahs will look to ‘justify’ their invitation to Europe’s top table when they begin their Challenge Cup journey against Section Paloise at the Stade du Hameau on Saturday. LIVE: RUGBY GAME 2022 STREAMING ONLINE Version 143 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.rtyrt The Cheetahs, one of the two South African sides debuting in the Challenge Cup this season, join the competition on an invitational basis. One of the conditions of the invitation is that they base themselves in Europe. The South African side will use Zebre’s ground, the Stadio Sergio Lanfranchi, for their home games, rather than welcoming sides to Bloemfontein. Cheetahs coach Hawies Fourie admitted there is a lot of pressure and expectation of the Cheetahs, given that they last played Northern Hemisphere opposition in February 2020 – when they lost 10-13 to the Dragons at Rodney Parade. 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.tryruj 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "eetrtuj 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.ytit 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.rtyrtuj This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:yu Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2. These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 501,088 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures: Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyu Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset. Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article. Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.tyt Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset. We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).tyu The work is based on the following publications:tuy COVID-19 Open Research Dataset (CORD-19). 2020. Version 2022-11-25 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2022-11-25. doi:10.5281/zenodo.3715506 Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2022-11-25) R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019 I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020) Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, and Kristina Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380 A Web user interface that uses these data to facilitate the COVID-19 literature exploration, can be found here. More details in our peer-reviewed publication here (also here there is an outdated preprint version).tuyt Funding: We acknowledge support of this work by the project "Moving from Big Data Management to Data Science" (MIS 5002437/3) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).tuyt 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.truy Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Gridtuyt (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doi All data is released in accordance with Elexon's disclaimer and reservation of rights. This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.tujty Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.tuyti The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,373,244,490 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (356,005,294 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/tuyt Wolf, Thomas; Debut, Lysandre; Sanh, Victor; Chaumond, Julien; Delangue, Clement; Moi, Anthony; Cistac, Perric; Ma, Clara; Jernite, Yacine; Plu, Julien; Xu, Canwen; Le Scao, Teven; Gugger, Sylvain; Drame, Mariama; Lhoest, Quentin; Rush, Alexander M.tut PyTorch 2.0 stack support We are very excited by the newly announced PyTorch 2.0 stack. You can enable torch.compile on any of our models, and get support with the Trainer (and in all our PyTorch examples) by using the torchdynamo training argument. For instance, just add --torchdynamo inductor when launching those examples from the command line. This API is still experimental and may be subject to changes as the PyTorch 2.0 stack matures. Note that to get the best performance, we recommend:yht using an Ampere GPU (or more recent) sticking to fixed shaped for now (so use --pad_to_max_length in our examples) Repurpose torchdynamo training args towards torch._dynamo by @sgugger in #20498 Audio Spectrogram Transformer The Audio Spectrogram Transformer model was proposed in AST: Audio Spectrogram Transformer by Yuan Gong, Yu-An Chung, James Glass. The Audio Spectrogram Transformer applies a Vision Transformer to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results for audio classification.tyuity Add Audio Spectogram Transformer by @NielsRogge in #19981 Jukebox The Jukebox model was proposed in Jukebox: A generative model for music by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditionned on an artist, genres and lyrics.tyuti Add Jukebox model (replaces #16875) by @ArthurZucker in #17826 Switch Transformers The SwitchTransformers model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity by William Fedus, Barret Zoph, Noam Shazeer. It is the first MoE model supported in transformers, with the largest checkpoint currently available currently containing 1T parameters.ytrtuj Add Switch transformers by @younesbelkada and @ArthurZucker in #19323 RocBert The RoCBert model was proposed in RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou. It's a pretrained Chinese language model that is robust under various forms of adversarial attacks.tyut Add RocBert by @sww9370 in #20013 CLIPSeg The CLIPSeg model was proposed in Image Segmentation Using Text and Image Prompts by Timo Lüddecke and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen CLIP model for zero- and one-shot image segmentation.rytru NAT was proposed in Neighborhood Attention Transformer by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.tyht It is a hierarchical vision transformer based on Neighborhood Attention, a sliding-window self attention pattern. DiNAT DiNAT was proposed in Dilated Neighborhood Attention Transformer by Ali Hassani and Humphrey Shi. It extends NAT by adding a Dilated Neighborhood Attention pattern to capture global context, and shows significant performance improvements over it.rytu Add Neighborhood Attention Transformer (NAT) and Dilated NAT (DiNAT) models by @alihassanijr in #20219 MobileNetV2 The MobileNet model was proposed in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.tryrtuj add MobileNetV2 model by @hollance in #17845 MobileNetV1 The MobileNet model was proposed in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.tyhu add MobileNetV1 model by @hollance in #17799 Image processors Image processors replace feature extractors as the processing class for computer vision models.rtyhtu Important changes: size parameter is now a dictionary of {"height": h, "width": w}, {"shortest_edge": s}, {"shortest_egde": s, "longest_edge": l} instead of int or tuple. Addition of data_format flag. You can now specify if you want your images to be returned in "channels_first" - NCHW - or "channels_last" - NHWC - format. Processing flags e.g. do_resize can be passed directly to the preprocess method instead of modifying the class attribute: image_processor([image_1, image_2], do_resize=False, return_tensors="pt", data_format="channels_last") Leaving return_tensors unset will return a list of numpy arrays. The classes are backwards compatible and can be created using existing feature extractor configurations - with the size parameter converted.tyr Add Image Processors by @amyeroberts in #19796 Add Donut image processor by @amyeroberts #20425 Add segmentation + object detection image processors by @amyeroberts in #20160 AutoImageProcessor by @amyeroberts in #20111 Backbone for computer vision models We're adding support for a general AutoBackbone class, which turns any vision model (like ConvNeXt, Swin Transformer) into a backbone to be used with frameworks like DETR and Mask R-CNN. The design is in early stages and we welcome feedback.tyu Add AutoBackbone + ResNetBackbone by @NielsRogge in #20229 Improve backbone by @NielsRogge in #20380 [AutoBackbone] Improve API by @NielsRogge in #20407 Support for safetensors offloading If the model you are using has a safetensors checkpoint and you have the library installed, offload to disk will take advantage of this to be more memory efficient and roughly 33% faster.dyhrtju Safetensors offload by @sgugger in #20321 Contrastive search in the generate method Generate: TF contrastive search with XLA support by @gante in #20050 Generate: contrastive search with full optional outputs by @gante in #19963 Breaking changes 🚨 🚨 🚨 Fix Issue 15003: SentencePiece Tokenizers Not Adding Special Tokens in convert_tokens_to_string by @beneyal in #15775 Bugfixes and improvements add dataset by @stevhliu in #20005 Add BERT resources by @stevhliu in #19852 Add LayoutLMv3 resource by @stevhliu in #19932 fix typo by @stevhliu in #20006 Update object detection pipeline to use post_process_object_detection methods by @alaradirik in #20004 clean up vision/text config dict arguments by @ydshieh in #19954 make sentencepiece import conditional in bertjapanesetokenizer by @ripose-jp in #20012 Fix gradient checkpoint test in encoder-decoder by @ydshieh in #20017 Quality by @sgugger in #20002 Update auto processor to check image processor created by @amyeroberts in #20021 [Doctest] Add configuration_deberta_v2.py by @Saad135 in #19995 Improve model tester by @ydshieh in #19984 Fix doctest by @ydshieh in #20023 Show installed libraries and their versions in CI jobs by @ydshieh in #20026 reorganize glossary by @stevhliu in #20010 Now supporting pathlike in pipelines too. by @Narsil in #20030 Add **kwargs by @amyeroberts in #20037 Fix some doctests after PR 15775 by @ydshieh in #20036 [Doctest] Add configuration_camembert.py by @Saad135 in #20039 [Whisper Tokenizer] Make more user-friendly by @sanchit-gandhi in #19921 [FuturWarning] Add futur warning for LEDForSequenceClassification by @ArthurZucker in #19066 fix jit trace error for model forward sequence is not aligned with jit.trace tuple input sequence, update related doc by @sywangyi in #19891 Update esmfold conversion script by @Rocketknight1 in #20028 Fixed torch.finfo issue with torch.fx by @michaelbenayoun in #20040 Only resize embeddings when necessary by @sgugger in #20043ty Speed up TF token classification postprocessing by converting complete tensors to numpy by @deutschmn in #19976 Fix ESM LM head test by @Rocketknight1 in #20045 Update README.md by @bofenghuang in #20063 fix tokenizer_type to avoid error when loading checkpoint back by @pacman100 in #20062 [Trainer] Fix model name in push_to_hub by @sanchit-gandhi