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--- |
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language: |
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- ru |
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- ru-RU |
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tags: |
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- conversational |
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--- |
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### 📝 Description |
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DialoGPT trained on Russian language and fine tuned on my telegram chat. |
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This model was created by [sberbank-ai](https://hf.co/sberbank-ai) and trained on Russian forums (see [Grossmend's model](https://hf.co/Grossmend/rudialogpt3_medium_based_on_gpt2)). You can find info about how it has been trained on [habr](https://habr.com/ru/company/icl_services/blog/548244/) (in Russian). I have created a **simple pipeline** and **fine tuned** that model on my own **exported telegram chat** (~30mb json). It is in fact very easy to get the data from telegram and fine tune a model. Therefore, I made a **colab tutorial** for it: https://colab.research.google.com/drive/1fnAVURjyZRK9VQg1Co_-SKUQnRES8l9R?usp=sharing |
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⚠️ Due to specifics of the data Hosted inference API may not work properly ⚠️ |
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🤗To try it use my [Spaces demo](https://huggingface.co/spaces/Kirili4ik/chat-with-Kirill)🤗 |
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### ❓ How to use with code |
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```python |
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# Download model and tokenizer |
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checkpoint = "Kirili4ik/ruDialoGpt3-medium-finetuned-telegram" |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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model = AutoModelForCausalLM.from_pretrained(checkpoint) |
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model.eval() |
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# util function to get expected len after tokenizing |
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def get_length_param(text: str, tokenizer) -> str: |
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tokens_count = len(tokenizer.encode(text)) |
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if tokens_count <= 15: |
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len_param = '1' |
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elif tokens_count <= 50: |
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len_param = '2' |
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elif tokens_count <= 256: |
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len_param = '3' |
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else: |
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len_param = '-' |
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return len_param |
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# util function to get next person number (1/0) for Machine or Human in the dialogue |
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def get_user_param(text: dict, machine_name_in_chat: str) -> str: |
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if text['from'] == machine_name_in_chat: |
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return '1' # machine |
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else: |
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return '0' # human |
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chat_history_ids = torch.zeros((1, 0), dtype=torch.int) |
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while True: |
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next_who = input("Who's phrase?\t") #input("H / G?") # Human or GPT |
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# In case Human |
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if next_who == "H" or next_who == "Human": |
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input_user = input("===> Human: ") |
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# encode the new user input, add parameters and return a tensor in Pytorch |
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new_user_input_ids = tokenizer.encode(f"|0|{get_length_param(input_user, tokenizer)}|" \ |
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+ input_user + tokenizer.eos_token, return_tensors="pt") |
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# append the new user input tokens to the chat history |
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chat_history_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) |
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if next_who == "G" or next_who == "GPT": |
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next_len = input("Phrase len? 1/2/3/-\t") #input("Exp. len?(-/1/2/3): ") |
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# encode the new user input, add parameters and return a tensor in Pytorch |
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new_user_input_ids = tokenizer.encode(f"|1|{next_len}|", return_tensors="pt") |
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# append the new user input tokens to the chat history |
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chat_history_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) |
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# print(tokenizer.decode(chat_history_ids[-1])) # uncomment to see full gpt input |
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# save previous len |
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input_len = chat_history_ids.shape[-1] |
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# generated a response; PS you can read about the parameters at hf.co/blog/how-to-generate |
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chat_history_ids = model.generate( |
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chat_history_ids, |
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num_return_sequences=1, # use for more variants, but have to print [i] |
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max_length=512, |
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no_repeat_ngram_size=3, |
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do_sample=True, |
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top_k=50, |
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top_p=0.9, |
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temperature = 0.6, # 0 for greedy |
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mask_token_id=tokenizer.mask_token_id, |
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eos_token_id=tokenizer.eos_token_id, |
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unk_token_id=tokenizer.unk_token_id, |
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pad_token_id=tokenizer.pad_token_id, |
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device='cpu' |
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) |
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# pretty print last ouput tokens from bot |
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print(f"===> GPT-3: {tokenizer.decode(chat_history_ids[:, input_len:][0], skip_special_tokens=True)}") |
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``` |
