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--- |
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license: mit |
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widget: |
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- text: "@@ПЕРВЫЙ@@ привет @@ВТОРОЙ@@ привет @@ПЕРВЫЙ@@ как дела? @@ВТОРОЙ@@" |
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example_title: "how r u" |
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- text: "@@ПЕРВЫЙ@@ что ты делал на выходных? @@ВТОРОЙ@@" |
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example_title: "wyd" |
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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 generation model is based on [sberbank-ai/rugpt3medium_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3medium_based_on_gpt2). It's trained on large corpus of dialog data and can be used for buildning generative conversational agents |
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The model was trained with context size 3 |
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On a private validation set we calculated metrics introduced in [this paper](https://arxiv.org/pdf/2001.09977.pdf): |
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- Sensibleness: Crowdsourcers were asked whether model's response makes sense given the context |
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- Specificity: Crowdsourcers were asked whether model's response is specific for given context, in other words we don't want our model to give general and boring responses |
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- SSA which is the average of two metrics above (Sensibleness Specificity Average) |
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| | sensibleness | specificity | SSA | |
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|:----------------------------------------------------|---------------:|--------------:|------:| |
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| [tinkoff-ai/ruDialoGPT-small](https://huggingface.co/tinkoff-ai/ruDialoGPT-small) | 0.64 | 0.5 | 0.57 | |
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| [tinkoff-ai/ruDialoGPT-medium](https://huggingface.co/tinkoff-ai/ruDialoGPT-medium) | 0.78 | 0.69 | 0.735 | |
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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, AutoModelWithLMHead |
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tokenizer = AutoTokenizer.from_pretrained('tinkoff-ai/ruDialoGPT-medium') |
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model = AutoModelWithLMHead.from_pretrained('tinkoff-ai/ruDialoGPT-medium') |
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inputs = tokenizer('@@ПЕРВЫЙ@@ привет @@ВТОРОЙ@@ привет @@ПЕРВЫЙ@@ как дела? @@ВТОРОЙ@@', return_tensors='pt') |
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generated_token_ids = model.generate( |
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**inputs, |
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top_k=10, |
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top_p=0.95, |
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num_beams=3, |
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num_return_sequences=3, |
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do_sample=True, |
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no_repeat_ngram_size=2, |
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temperature=1.2, |
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repetition_penalty=1.2, |
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length_penalty=1.0, |
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eos_token_id=50257, |
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max_new_tokens=40 |
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) |
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context_with_response = [tokenizer.decode(sample_token_ids) for sample_token_ids in generated_token_ids] |
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context_with_response |
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``` |
