71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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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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import torch
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from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
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from .base import PipelineTool
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class TextClassificationTool(PipelineTool):
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"""
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Example:
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```py
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from transformers.tools import TextClassificationTool
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classifier = TextClassificationTool()
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classifier("This is a super nice API!", labels=["positive", "negative"])
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```
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"""
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default_checkpoint = "facebook/bart-large-mnli"
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description = (
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"This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
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"should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
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"It returns the most likely label in the list of provided `labels` for the input text."
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)
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name = "text_classifier"
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pre_processor_class = AutoTokenizer
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model_class = AutoModelForSequenceClassification
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inputs = ["text", ["text"]]
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outputs = ["text"]
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def setup(self):
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super().setup()
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config = self.model.config
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self.entailment_id = -1
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for idx, label in config.id2label.items():
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if label.lower().startswith("entail"):
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self.entailment_id = int(idx)
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if self.entailment_id == -1:
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raise ValueError("Could not determine the entailment ID from the model config, please pass it at init.")
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def encode(self, text, labels):
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self._labels = labels
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return self.pre_processor(
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[text] * len(labels),
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[f"This example is {label}" for label in labels],
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return_tensors="pt",
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padding="max_length",
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)
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def decode(self, outputs):
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logits = outputs.logits
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label_id = torch.argmax(logits[:, 2]).item()
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return self._labels[label_id]
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