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Browse files- pair_classification_tool.py +23 -0
- tool_config.json +7 -0
pair_classification_tool.py
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import torch
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from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer
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from transformers.tools import PipelineTool
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class TextPairClassificationTool(PipelineTool):
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default_checkpoint = "sgugger/bert-finetuned-mrpc"
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pre_processor_class = AutoTokenizer
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model_class = AutoModelForSequenceClassification
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description = (
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"This is a tool that classifies if two texts in English are similar or not using the labels 'equivalent' and "
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"'not_equivalent'. It takes two inputs named `text` and `second_text` which should be in English and returns "
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"the predicted label."
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)
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def encode(self, text, second_text):
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return self.pre_processor(text, second_text, return_tensors="pt")
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def decode(self, outputs):
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logits = outputs.logits
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label_id = torch.argmax(logits[0]).item()
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return self.model.config.id2label[label_id]
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tool_config.json
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{
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"text_pair_classification": {
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"description": "This is a tool that classifies if two texts in English are similar or not using the labels 'equivalent' and 'not_equivalent'. It takes two inputs named `text` and `second_text` which should be in English and returns the predicted label.",
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"name": "pair_classifier",
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"tool_class": "pair_classification_tool.TextPairClassificationTool"
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}
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}
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