---
license: mit
widget:
- text: "привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]супер, вот только проснулся, у тебя как?"
  example_title: "Dialog example 1"
- text: "привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]норм"
  example_title: "Dialog example 2"
- text: "привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]норм, у тя как?"
  example_title: "Dialog example 3"
---

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 dialogue.

The labels explanation:
- `relevance`: is the last message in the dialogue relevant in the context of the full dialogue
- `specificity`: is the last message in the dialogue interesting and promotes the continuation of the dialogue

The preferable length of the dialogue is 4 where the last message is needed to be estimated

It is pretrained on corpus of dialog data 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):


|             |   threshold |   f0.5 |   ROC AUC |
|:------------|------------:|-------:|----------:|
| relevance   |        0.51 |   0.82 |      0.74 |
| specificity |        0.54 |   0.81 |      0.8  |


The preferable usage:

```python
# pip install transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("/mnt/chatbot_models2/chit-chat/experiments/crossencoder_hf/rubert-base-sentence/dialogs_whole")
model = AutoModelForSequenceClassification.from_pretrained("/mnt/chatbot_models2/chit-chat/experiments/crossencoder_hf/rubert-base-sentence/dialogs_whole")
# model.cuda()
inputs = tokenizer('привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]норм',
                   padding=True, max_length=128, truncation=True, add_special_tokens=False, return_tensors='pt')
with torch.inference_mode():
    logits = model(**inputs).logits
    probas = torch.sigmoid(logits)[0].cpu().detach().numpy()
print(probas)
```