| import gradio as gr |
| import torch |
| from transformers import BertJapaneseTokenizer, BertForSequenceClassification |
|
|
| |
| MODEL_NAME = 'cl-tohoku/bert-base-japanese-whole-word-masking' |
| descriptions = '''BERTを用いたビジネス文書のネガポジ判定。文章を入力すると、その文章のネガポジ判定と判定の信頼度を表示します。''' |
|
|
| tokenizer = BertJapaneseTokenizer.from_pretrained(MODEL_NAME) |
| bert_sc_ = BertForSequenceClassification.from_pretrained("./models") |
| bert_sc = bert_sc_.to("cpu") |
|
|
| def func(text): |
| encoding = tokenizer( |
| text, |
| padding = "longest", |
| return_tensors="pt" |
| ) |
| encoding = { k : v.cpu() for k, v in encoding.items()} |
| |
| with torch.no_grad(): |
| output = bert_sc(**encoding) |
| scores = output.logits.argmax(-1) |
| neg = torch.softmax(output.logits, dim=1).tolist()[0][0] |
| pos = torch.softmax(output.logits, dim=1).tolist()[0][1] |
| |
| label = "ネガティブ" if scores.item()==0 else "ポジティブ" |
| cos = f"信頼度:{neg*100:.1f}%" if scores.item()==0 else f"信頼度:{pos*100:.1f}%" |
|
|
| return label,cos |
|
|
| app = gr.Interface(fn=func, inputs=gr.Textbox(lines=3, placeholder="文章を入力してください"), outputs=["label","label"], title="Sentiment Analysis of Business Documents", description=descriptions) |
| app.launch() |
