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