File size: 2,117 Bytes
dd96e9e cce806f dd96e9e cce806f 887c97f bcb833c 887c97f cce806f 13eed6f cce806f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
---
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.
It achieves the 0.83 F1 score on the gold test set from our [reply recovery dataset](https://data.mendeley.com/datasets/xm86yszck2).
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("astromis/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}
}
``` |