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---
license: mit
---
This classification model is based on [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2).
The model should be used to produce relevance and specificity of the last message in the context of a dialog.
It is pretrained on corpus of dialog data from social networks and finetuned on [tinkoff-ai/context_similarity](https://huggingface.co/tinkoff-ai/context_similarity).
The performance of the model on validation split [tinkoff-ai/context_similarity](https://huggingface.co/tinkoff-ai/context_similarity) (with the best thresholds for validation samples):
<table>
<thead>
<tr>
<td colspan="2"><center>relevance</center></td>
<td colspan="2"><center>specificity</center></td>
</tr>
</thead>
<tbody>
<tr>
<td><center>f0.5</center></td>
<td><center>roc-auc</center></td>
<td><center>f0.5</center></td>
<td><center>roc-auc</center></td>
</tr>
<tr>
<td><center>0.82</center></td>
<td><center>0.74</center></td>
<td><center>0.81</center></td>
<td><center>0.8</center></td>
</tr>
</tbody>
</table>
The model can be loaded as follows:
```python
# pip install transformers
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("tinkoff-ai/context_similarity")
model = AutoModel.from_pretrained("tinkoff-ai/context_similarity")
# model.cuda()
``` |