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--- |
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license: mit |
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widget: |
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- text: "[CLS]привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]супер, вот только проснулся, у тебя как?" |
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example_title: "Dialog example 1" |
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- text: "[CLS]привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]норм" |
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example_title: "Dialog example 2" |
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- text: "[CLS]привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]норм, у тя как?" |
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example_title: "Dialog example 3" |
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language: |
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- ru |
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tags: |
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- conversational |
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--- |
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This classification model is based on [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co./DeepPavlov/rubert-base-cased-sentence). |
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The model should be used to produce relevance and specificity of the last message in the context of a dialogue. |
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The labels explanation: |
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- `relevance`: is the last message in the dialogue relevant in the context of the full dialogue. |
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- `specificity`: is the last message in the dialogue interesting and promotes the continuation of the dialogue. |
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It is pretrained on a large corpus of dialog data in unsupervised manner: the model is trained to predict whether last response was in a real dialog, or it was pulled from some other dialog at random. |
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Then it was finetuned on manually labelled examples (dataset will be posted soon). |
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The model was trained with three messages in the context and one response. Each message was tokenized separately with ``` max_length = 32 ```. |
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The performance of the model on validation split (dataset will be posted soon) (with the best thresholds for validation samples): |
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| | threshold | f0.5 | ROC AUC | |
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|:------------|------------:|-------:|----------:| |
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| relevance | 0.49 | 0.84 | 0.79 | |
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| specificity | 0.53 | 0.83 | 0.83 | |
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How to use: |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained('tinkoff-ai/response-quality-classifier-base') |
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model = AutoModelForSequenceClassification.from_pretrained('tinkoff-ai/response-quality-classifier-base') |
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inputs = tokenizer('[CLS]привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]норм, у тя как?', max_length=128, add_special_tokens=False, return_tensors='pt') |
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with torch.inference_mode(): |
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logits = model(**inputs).logits |
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probas = torch.sigmoid(logits)[0].cpu().detach().numpy() |
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relevance, specificity = probas |
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``` |
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The [app](https://huggingface.co./spaces/tinkoff-ai/response-quality-classifiers) where you can easily interact with this model. |
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The work was done during internship at Tinkoff by [egoriyaa](https://github.com/egoriyaa), mentored by [solemn-leader](https://huggingface.co./solemn-leader). |
