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https://api-inference.huggingface.co/models/shrugging-grace/tweetclassifier
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shrugging-grace/tweetclassifier shrugging-grace/tweetclassifier
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pytorch

tf

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shrugging-grace jme-p
1 model

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shrugging-grace/tweetclassifier") model = AutoModelForSequenceClassification.from_pretrained("shrugging-grace/tweetclassifier")

shrugging-grace/tweetclassifier

Model description

This model classifies tweets as either relating to the Covid-19 pandemic or not.

Intended uses & limitations

It is intended to be used on tweets commenting on UK politics, in particular those trending with the #PMQs hashtag, as this refers to weekly Prime Ministers' Questions.

How to use

LABEL_0 means that the tweet relates to Covid-19

LABEL_1 means that the tweet does not relate to Covid-19

Training data

The model was trained on 1000 tweets (with the "#PMQs'), which were manually labeled by the author. The tweets were collected between May-July 2020.

BibTeX entry and citation info

This was based on a pretrained version of BERT.

@article{devlin2018bert, title={Bert: Pre-training of deep bidirectional transformers for language understanding}, author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1810.04805}, year={2018} }