initial commit

app.py

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+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import gradio as gr
+
+tokenizer = AutoTokenizer.from_pretrained("jarvisx17/japanese-sentiment-analysis")
+model = AutoModelForSequenceClassification.from_pretrained("jarvisx17/japanese-sentiment-analysis")
+
+classes = ["negative", "positive"]
+
+def predict(sequence):
+  input = tokenizer(sequence, return_tensors="pt")
+  classification_logits = model(**input).logits
+  results = torch.softmax(classification_logits, dim=1).tolist()[0]
+
+  output = classes[0] + ":" + str(int(round(results[0] * 100))) + "\n"
+  output += classes[1] + ":" + str(int(round(results[1] * 100)))
+
+  return output
+
+gr.Interface(fn=predict, inputs="text", outputs="text").launch()

requirements.txt

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+datasets
+evaluate
+transformers[sentencepiece]
+transformers
+sentencepiece
+fugashi
+fugashi[unidic]
+ipadic