Update README.md

README.md

@@ -1,3 +1,49 @@
 ---
 license: mit
+language:
+- ru
+metrics:
+- f1
+library_name: transformers
+tags:
+- russian
+- conversation
+- chats
+- embeddings
+- coherence
 ---
+# Model Card
+
+This model is trained to predict whether two given messages from some group chat with many members can have a `reply_to` relationship.
+
+# Training details
+
+It's based on [Conversational RuBERT](https://docs.deeppavlov.ai/en/master/features/models/bert.html) (cased, 12-layer, 768-hidden, 12-heads, 180M parameters) that was trained on several social media datasets. We fine-tuned it with the data from several Telegram chats. The positive `reply_to` examples were obtained by natural user annotation. The negative ones were obtained by shuffling the messages. 
+The task perfectly aligns with the Next Sentence Prediction task, so the fine-tuning was done in that manner. See the [paper](https://www.dialog-21.ru/media/5871/buyanoviplusetal046.pdf) for more details.
+
+# Usage
+
+**Note:** if two messages have `reply_to` relationship, then **they have "zero" label**. This is because of the NSP formulation.
+```python
+from transformers import AutoTokenizer, BertForNextSentencePrediction
+tokenizer = AutoTokenizer.from_pretrained("rubert_reply_recovery", )
+model = BertForNextSentencePrediction.from_pretrained("rubert_reply_recovery", )
+
+inputs = tokenizer(['Где можно получить СНИЛС?', 'Я тут уже много лет'], ["Можете в МФЦ", "Куда отправить это письмо?"], return_tensors='pt',
+                                truncation=True, max_length=512, padding = 'max_length',)
+output = model(**inputs)
+print(output.logits.argmax(dim=1))
+# tensor([0, 1])
+```
+
+
+# Citation
+
+```bibtex
+@article{Buyanov2023WhoIA,
+  title={Who is answering to whom? Modeling reply-to relationships in Russian asynchronous chats},
+  author={Igor Buyanov and Darya Yaskova and Ilya Sochenkov},
+  journal={Computational Linguistics and Intellectual Technologies},
+  year={2023}
+}
+```