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Migrate model card from transformers-repo

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Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/digitalepidemiologylab/covid-twitter-bert/README.md

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  1. README.md +33 -3
README.md CHANGED
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  # COVID-Twitter-BERT (CT-BERT) v1
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- BERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19.
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- Find more info on our [GitHub page](https://github.com/digitalepidemiologylab/covid-twitter-bert).
 
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  ## Overview
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  This model was trained on 160M tweets collected between January 12 and April 16, 2020 containing at least one of the keywords "wuhan", "ncov", "coronavirus", "covid", or "sars-cov-2". These tweets were filtered and preprocessed to reach a final sample of 22.5M tweets (containing 40.7M sentences and 633M tokens) which were used for training.
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- This model was evaluated based on downstream classification tasks, but it could be used for any other NLP task which can leverage contextual embeddings.
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  In order to achieve best results, make sure to use the same text preprocessing as we did for pretraining. This involves replacing user mentions, urls and emojis. You can find a script on our projects [GitHub repo](https://github.com/digitalepidemiologylab/covid-twitter-bert).
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@@ -17,5 +27,25 @@ tokenizer = AutoTokenizer.from_pretrained("digitalepidemiologylab/covid-twitter-
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  model = AutoModel.from_pretrained("digitalepidemiologylab/covid-twitter-bert")
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  ```
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  ## References
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  [1] Martin Müller, Marcel Salaté, Per E Kummervold. "COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter" arXiv preprint arXiv:2005.07503 (2020).
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+ ---
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+ language: "en"
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+ thumbnail: "https://raw.githubusercontent.com/digitalepidemiologylab/covid-twitter-bert/master/images/COVID-Twitter-BERT_small.png"
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+ tags:
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+ - Twitter
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+ - COVID-19
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+ license: mit
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+ ---
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+
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  # COVID-Twitter-BERT (CT-BERT) v1
 
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+ :warning: _You may want to use the [v2 model](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) which was trained on more recent data and yields better performance_ :warning:
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+
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+ BERT-large-uncased model, pretrained on a corpus of messages from Twitter about COVID-19. Find more info on our [GitHub page](https://github.com/digitalepidemiologylab/covid-twitter-bert).
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  ## Overview
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  This model was trained on 160M tweets collected between January 12 and April 16, 2020 containing at least one of the keywords "wuhan", "ncov", "coronavirus", "covid", or "sars-cov-2". These tweets were filtered and preprocessed to reach a final sample of 22.5M tweets (containing 40.7M sentences and 633M tokens) which were used for training.
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+ This model was evaluated based on downstream classification tasks, but it could be used for any other NLP task which can leverage contextual embeddings.
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  In order to achieve best results, make sure to use the same text preprocessing as we did for pretraining. This involves replacing user mentions, urls and emojis. You can find a script on our projects [GitHub repo](https://github.com/digitalepidemiologylab/covid-twitter-bert).
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  model = AutoModel.from_pretrained("digitalepidemiologylab/covid-twitter-bert")
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  ```
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+ You can also use the model with the `pipeline` interface:
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+
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+ ```python
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+ from transformers import pipeline
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+ import json
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+
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+ pipe = pipeline(task='fill-mask', model='digitalepidemiologylab/covid-twitter-bert-v2')
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+ out = pipe(f"In places with a lot of people, it's a good idea to wear a {pipe.tokenizer.mask_token}")
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+ print(json.dumps(out, indent=4))
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+ [
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+ {
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+ "sequence": "[CLS] in places with a lot of people, it's a good idea to wear a mask [SEP]",
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+ "score": 0.9959408044815063,
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+ "token": 7308,
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+ "token_str": "mask"
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+ },
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+ ...
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+ ]
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+ ```
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+
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  ## References
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  [1] Martin Müller, Marcel Salaté, Per E Kummervold. "COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter" arXiv preprint arXiv:2005.07503 (2020).