Instructions to use shrugging-grace/tweetclassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shrugging-grace/tweetclassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shrugging-grace/tweetclassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shrugging-grace/tweetclassifier") model = AutoModelForSequenceClassification.from_pretrained("shrugging-grace/tweetclassifier") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
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} }
- Downloads last month
- 3