Add README

README.md

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+---
+language:
+- en
+thumbnail: "https://raw.githubusercontent.com/digitalepidemiologylab/covid-twitter-bert/master/images/COVID-Twitter-BERT_small.png"
+tags:
+- Twitter
+- COVID-19
+- text-classification
+- pytorch
+- tensorflow
+- bert
+license: MIT
+datasets:
+- mnli
+pipeline_tag: zero-shot-classification
+widget:
+- text: "To stop the pandemic it is important that everyone turns up for their shots."
+  candidate_labels: "health, sport, vaccine, guns"
+---
+
+# COVID-Twitter-BERT v2 MNLI
+
+## Model description
+This model provides a zero-shot classifier to be used in cases where it is not possible to finetune CT-BERT on a specific task, due to lack of labelled data.
+
+The technique is based on [Yin et al.](https://arxiv.org/abs/1909.00161).
+The article describes a very clever way of using pre-trained MNLI models as zero-shot sequence classifiers.
+The model is already finetuned on 400'000 generaic logical tasks.
+We can then use it as a zero-shot classifier by reformulating the classification task as a question.
+
+Let's say we want to classify COVID-tweets as vaccine-related and not vaccine-related.
+The typical way would be to collect a few hunder pre-annotated tweets and organise them in two classes.
+Then you would finetune the model on this.
+
+With the zero-shot mnli-classifier, you can instead reformulate your question as "This text is about vaccines", and use this directly on inference - without any training.
+
+Find more info about the model on our [GitHub page](https://github.com/digitalepidemiologylab/covid-twitter-bert).
+
+## Usage
+Please note that how you formulate the question can give slightly different results.
+Collecting a training set and finetuning on this, will most likely give you better accuracy.
+
+The easiest way to try this out is by using the Hugging Face pipeline.
+This uses the default Enlish template where it puts the text "This example is " in front of the text.
+
+```python
+from transformers import pipeline
+classifier = pipeline("zero-shot-classification", model="digitalepidemiologylab/covid-twitter-bert-v2-mnli")
+```
+You can then use this pipeline to classify sequences into any of the class names you specify.
+```python
+sequence_to_classify = 'To stop the pandemic it is important that everyone turns up for their shots.'
+candidate_labels = ['health', 'sport', 'vaccine','guns']
+hypothesis_template = 'This example is {}.'
+classifier(sequence_to_classify, candidate_labels, hypothesis_template=hypothesis_template, multi_class=True)
+```
+
+## Training procedure
+The model is finetuned on the 400k large [MNLI-task](https://cims.nyu.edu/~sbowman/multinli/).
+
+## References
+```bibtex
+@article{muller2020covid,
+  title={COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter},
+  author={M{\"u}ller, Martin and Salath{\'e}, Marcel and Kummervold, Per E},
+  journal={arXiv preprint arXiv:2005.07503},
+  year={2020}
+}
+```
+or
+```
+Martin Müller, Marcel Salathé, and Per E. Kummervold.
+COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter.
+arXiv preprint arXiv:2005.07503 (2020).
+```