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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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import numpy as np |
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import gradio as gr |
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import os |
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SECRET_TOKEN = os.getenv("TOKEN_OFFENSIVENESS_ESTIMATION") |
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tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-japanese-base-lite") |
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model = AutoModelForSequenceClassification.from_pretrained("TomokiFujihara/luke-japanese-base-lite-offensiveness-estimation", trust_remote_code=True, use_auth_token=SECRET_TOKEN) |
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def generate(text): |
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inputs = tokenizer.encode_plus(text, return_tensors='pt') |
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outputs = model(inputs['input_ids'], inputs['attention_mask']).detach().numpy()[0][:3] |
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minimum = np.min(outputs) |
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if minimum < 0: |
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outputs = outputs - minimum |
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score = outputs / np.sum(outputs) |
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prediction = f'攻撃的でない発言: {score[0]:.1%},\nグレーゾーンの発言: {score[1]:.1%},\n攻撃的な発言: {score[2]:.1%}' |
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return prediction |
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iface = gr.Interface( |
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generate, |
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inputs = gr.Textbox(label = "Input a text", value = "攻撃性を評価したいコメントを入力してください."), |
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outputs="text", |
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title = "日本語のSNSコメントの攻撃性推定") |
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iface.launch(share=True) |
