mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-23 06:12:50 +08:00
50 lines
1.7 KiB
Python
50 lines
1.7 KiB
Python
# Copyright 2024 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Dict, Optional
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import numpy as np
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from ...extras.misc import numpify
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if TYPE_CHECKING:
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from transformers import EvalPrediction
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@dataclass
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class ComputeAccuracy:
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def _dump(self) -> Optional[Dict[str, float]]:
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result = None
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if hasattr(self, "score_dict"):
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result = {k: float(np.mean(v)) for k, v in self.score_dict.items()}
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self.score_dict = {"accuracy": []}
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return result
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def __post_init__(self):
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self._dump()
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def __call__(self, eval_preds: "EvalPrediction", compute_result: bool = True) -> Optional[Dict[str, float]]:
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chosen_scores, rejected_scores = numpify(eval_preds.predictions[0]), numpify(eval_preds.predictions[1])
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if not chosen_scores.shape:
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self.score_dict["accuracy"].append(chosen_scores > rejected_scores)
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else:
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for i in range(len(chosen_scores)):
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self.score_dict["accuracy"].append(chosen_scores[i] > rejected_scores[i])
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if compute_result:
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return self._dump()
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