31 lines
1.2 KiB
Python
31 lines
1.2 KiB
Python
from transformers import TextGenerationPipeline
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from transformers.pipelines.text_generation import ReturnType
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human = "<human>:"
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bot = "<bot>:"
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# human-bot interaction like OIG dataset
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prompt = """{human} {instruction}
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{bot}""".format(
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human=human,
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instruction="{instruction}",
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bot=bot,
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)
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class H2OTextGenerationPipeline(TextGenerationPipeline):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def preprocess(self, prompt_text, prefix="", handle_long_generation=None, **generate_kwargs):
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prompt_text = prompt.format(instruction=prompt_text)
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return super().preprocess(prompt_text, prefix=prefix, handle_long_generation=handle_long_generation,
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**generate_kwargs)
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def postprocess(self, model_outputs, return_type=ReturnType.FULL_TEXT, clean_up_tokenization_spaces=True):
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records = super().postprocess(model_outputs, return_type=return_type,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces)
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for rec in records:
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rec['generated_text'] = rec['generated_text'].split(bot)[1].strip().split(human)[0].strip()
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return records
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