second

app.py

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+import gradio as gr
+import torch
+from transformers import BertJapaneseTokenizer, BertForSequenceClassification
+
+# 日本語の事前学習モデル
+MODEL_NAME = 'cl-tohoku/bert-base-japanese-whole-word-masking'
+descriptions = '''BERTをchABSA-datasetでファインチューニングしたもの。
+chABSA-datasetは上場企業の有価証券報告書をベースに作成されたネガポジ用データセット'''
+
+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)
+    
+    label = "ネガティブ" if scores.item()==0 else "ポジティブ"
+
+    return label,text
+
+app = gr.Interface(fn=func, inputs="text", outputs=["label","text"], title="ビジネス文書／ネガポジ分析", description=descriptions)
+app.launch()

requirements.txt

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+torch
+transformers==4.20.0
+sentencepiece