41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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from typing import List
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from pydantic import BaseModel
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from fastapi import FastAPI
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from evaluate import load
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from comet import download_model, load_from_checkpoint
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app = FastAPI()
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class SummarizationEvaluationRequest(BaseModel):
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sources: List[str]
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summaries: List[str]
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class TranslationEvaluationRequest(BaseModel):
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sources: List[str]
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translations: List[str]
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@app.post("/bert-score/")
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def bert_score(request: SummarizationEvaluationRequest):
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bertscore = load("bertscore")
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return bertscore.compute(predictions=request.summaries, references=request.sources, lang="en")
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@app.post("/meteor-score/")
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def meteor_score(request: SummarizationEvaluationRequest):
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meteor = load("meteor")
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return meteor.compute(predictions=request.summaries, references=request.sources)
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@app.post("/bleu-score/")
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def bleu_score(request: SummarizationEvaluationRequest):
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bleu = load("bleu")
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return bleu.compute(predictions=request.summaries, references=request.sources)
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@app.post("/comet-score/")
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def comet_score(request: TranslationEvaluationRequest):
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model_path = download_model("Unbabel/wmt22-cometkiwi-da")
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model = load_from_checkpoint(model_path)
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data = [{"src": src, "mt": mt} for src, mt in zip(request.sources, request.translations)]
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return model.predict(data, accelerator="cpu")
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