Spaces:
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add model
Browse files- app.py +15 -4
- requirements.txt +3 -1
app.py
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from fastapi import FastAPI
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app = FastAPI()
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from fastapi import FastAPI, Request
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from pydantic import BaseModel
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from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
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app = FastAPI()
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tokenizer = AutoTokenizer.from_pretrained("NlpHUST/vi-word-segmentation")
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model = AutoModelForTokenClassification.from_pretrained("NlpHUST/vi-word-segmentation")
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nlp = pipeline("token-classification", model=model, tokenizer=tokenizer)
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class InputText(BaseModel):
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text: str
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@app.post("/segment")
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async def segment_text(payload: InputText):
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text = payload.text
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result = nlp(text)
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return result
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requirements.txt
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fastapi
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uvicorn[standard]
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fastapi
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uvicorn[standard]
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transformers
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torch
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