Spaces:
Sleeping
Sleeping
T.Masuda
commited on
Commit
·
080cf4b
1
Parent(s):
3a90fd0
create app
Browse files- README.md +1 -1
- app.py +56 -0
- requirements.txt +6 -0
README.md
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---
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-
title: Question Answering
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emoji: 🏃
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colorFrom: green
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colorTo: red
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---
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title: Question Answering JA
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emoji: 🏃
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colorFrom: green
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colorTo: red
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app.py
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import gradio as gr
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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from datetime import datetime
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print('{}:loading...'.format(datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
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tokenizer = AutoTokenizer.from_pretrained('line-corporation/japanese-large-lm-1.7b-instruction-sft', use_fast=False)
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model = AutoModelForCausalLM.from_pretrained('line-corporation/japanese-large-lm-1.7b-instruction-sft')
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#tokenizer = AutoTokenizer.from_pretrained('line-corporation/japanese-large-lm-3.6b-instruction-sft', use_fast=False)
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#model = AutoModelForCausalLM.from_pretrained('line-corporation/japanese-large-lm-3.6b-instruction-sft')
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if torch.cuda.is_available():
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model.half()
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model = model.to('cuda')
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generator = pipeline('text-generation', model=model, tokenizer=tokenizer, device=model.device)
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print('{}:done.'.format(datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
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def generate(input_text, maxlen):
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input = f'ユーザー: {input_text}\nシステム: '
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output = generator(
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input,
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max_length=maxlen,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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top_k=0,
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repetition_penalty=1.1,
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num_beams=1,
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num_return_sequences=1,
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pad_token_id=tokenizer.pad_token_id,
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bos_token_id=tokenizer.bos_token_id,
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eos_token_id=tokenizer.eos_token_id
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)
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generated_text = output[0]['generated_text'][len(input) + 1:]
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return generated_text
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with gr.Blocks(title='question answering ja') as chatbox:
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gr.Markdown('# Question Answering JA')
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chatbot = gr.Chatbot(label='answer')
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msg = gr.Textbox(label='question')
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maxlen = gr.Slider(minimum=30, maximum=256, value=30, step=1, label='max length')
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clear = gr.ClearButton([msg, chatbot])
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def respond(message, maxlen, chat_history):
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if message == '':
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return '', chat_history
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bot_message = generate(message, maxlen)
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chat_history.append((message, bot_message))
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return '', chat_history
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msg.submit(respond, [msg, maxlen, chatbot], [msg, chatbot])
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chatbox.launch()
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requirements.txt
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gradio
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torch
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torchvision
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torchaudio
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transformers
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sentencepiece
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