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--- |
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language: |
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- ko |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: t5-base-korean-chit-chat |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# t5-base-korean-chit-chat |
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This model is a fine-tuning of paust/pko-t5-base model using AIHUB "ํ๊ตญ์ด SNS". This model infers the next conversation by using the conversation used on social media.. |
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์ด ๋ชจ๋ธ์ paust/pko-t5-large model์ AIHUB "ํ๊ตญ์ด SNS"๋ฅผ ์ด์ฉํ์ฌ fine tunning ํ ๊ฒ์
๋๋ค. ์ด ๋ชจ๋ธ์ SNS์์์ ์ฌ์ฉ๋๋ ๋ํ๋ฅผ ์ด์ฉํ์ฌ ๋ค์ ๋ํ๋ฅผ ์ถ๋ก ํฉ๋๋ค. |
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## Usage |
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```python |
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from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer, MT5ForConditionalGeneration |
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from transformers import AutoTokenizer, T5TokenizerFast |
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import nltk |
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nltk.download('punkt') |
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model_dir = f"lcw99/t5-base-korean-chit-chat" |
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max_input_length = 1024 |
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text = """ |
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A: ์ผํํ๋ฌ ๊ฐ๊น? B: ์ ์ข์. A: ์ธ์ ๊ฐ๊น? B: |
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""" |
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inputs = [text] |
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tokenizer = AutoTokenizer.from_pretrained(model_dir) |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_dir) |
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inputs = tokenizer(inputs, max_length=max_input_length, truncation=True, return_tensors="pt") |
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output = model.generate(**inputs, num_beams=3, do_sample=True, min_length=20, max_length=500, num_return_sequences=3) |
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for i in range(3): |
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#print(output[i]) |
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print("---", i) |
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decoded_output = tokenizer.decode(output[i], skip_special_tokens=True) |
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predicted_title = nltk.sent_tokenize(decoded_output) |
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#print(decoded_output) |
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print(predicted_title) |
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import torch |
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chat_history = [] |
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# Let's chat for 5 lines |
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for step in range(100): |
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print("") |
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user_input = input(">> User: ") |
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chat_history.append("A: " + user_input) |
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while len(chat_history) > 5: |
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chat_history.pop(0) |
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hist = "" |
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for chat in chat_history: |
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hist += "\n" + chat |
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hist += "\nB: " |
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new_user_input_ids = tokenizer.encode(hist, return_tensors='pt') |
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bot_input_ids = new_user_input_ids |
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# generated a response while limiting the total chat history to 1000 tokens, |
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chat_history_ids = model.generate( |
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bot_input_ids, max_length=200, |
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pad_token_id=tokenizer.eos_token_id, |
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do_sample=True, |
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#top_k=100, |
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#top_p=0.7, |
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#temperature = 0.1 |
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) |
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bot_text = tokenizer.decode(chat_history_ids[0], skip_special_tokens=True).replace("#@์ด๋ฆ#", "OOO") |
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bot_text = bot_text.replace("\n", " / ") |
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chat_history.append("B: " + bot_text) |
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# pretty print last ouput tokens from bot |
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print("Bot: {}".format(bot_text)) |
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``` |
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### Framework versions |
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- Transformers 4.22.1 |
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- TensorFlow 2.10.0 |
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- Datasets 2.5.1 |
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- Tokenizers 0.12.1 |
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