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USA Wrestling is pleased to announce that United World Wrestling has awarded both the 2022 and 2023 Men’s and Women’s Freestyle World Cup events to Xtream Arena in Coralville, Iowa. The 2022 competition will be held December 10-11, and the 2023 competition is set for December 9-10. LIVE: WRESTLING 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 World Cup is the annual international dual meet championships. This will be the first time in history that the Men’s Freestyle World Cup and the Women’s Freestyle World Cup events will be held side-by-side. The top five countries who have qualified for the 2022 Men’s Freestyle World Cup are the United States, Iran, Japan, Georgia, and Mongolia. The top five countries who have qualified for the 2022 Women’s Freestyle World Cup are Japan, United States, China, Mongolia, and Ukraine. Each side also has an All-World Team represented by top athletes whose countries did not qualify. Qualifying countries are determined based upon the overall team results from the Senior World Championships events held earlier each year. The 2022 Senior World Championships were held in Belgrade, Serbia in September. The 2023 Senior World Championships will be hosted in Russia. 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 in #20067 Update documentation on seq2seq models with absolute positional embeddings, to be in line with Tips section for BERT and GPT2 by @jordiclive in #20068 Allow passing arguments to model testers for CLIP-like models by @ydshieh in #20044 Show installed libraries and their versions in GA jobs by @ydshieh in #20069 Update defaults and logic to match old FE by @amyeroberts in #20065 Update modeling_tf_utils.py by @cakiki in #20076 Update hub.py by @cakiki in #20075 [Doctest] Add configuration_dpr.py by @Saad135 in #20080 Removing RobertaConfig inheritance from CamembertConfig by @Saad135 in #2005sdgt More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter) and our pre-print about the dataset (https://arxiv.org/abs/2004.03688)dfyj As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data ONLY for research purposes. They need to be hydrated to be usedyhtujfdff