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from typing import List, Any |
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import tiktoken |
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class AbstractCompressor: |
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base_model = None |
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tokenizer = None |
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gpt_tokenizer = tiktoken.encoding_for_model("gpt-3.5-turbo-16k") |
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def compress(self, original_prompt: str, ratio: float) -> dict: |
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""" |
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Input original prompt/sentence and compression ratio, return compressed prompt/sentence.\ |
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:param original_prompt: |
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:param ratio: |
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:return: dict object |
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""" |
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raise NotImplementedError() |
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def fit(self, datas: List[dict], valid_size: int) -> None: |
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""" |
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For trainable methods, call this function for training parameters. |
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Require training LongBench and valid set size. |
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:param datas: |
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:param valid_size: |
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:return: |
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""" |
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raise NotImplementedError() |
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def set_model(self, model: Any, **kwargs): |
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""" |
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Specify a trained or a pre-trained model. |
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:param model: |
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:param kwargs: |
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:return: |
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""" |
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pass |
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