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| #!/usr/bin/env python | |
| # coding=utf-8 | |
| # Copyright 2023 The HuggingFace Inc. team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import torch | |
| from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer | |
| from .base import PipelineTool | |
| class TextClassificationTool(PipelineTool): | |
| """ | |
| Example: | |
| ```py | |
| from transformers.tools import TextClassificationTool | |
| classifier = TextClassificationTool() | |
| classifier("This is a super nice API!", labels=["positive", "negative"]) | |
| ``` | |
| """ | |
| default_checkpoint = "facebook/bart-large-mnli" | |
| description = ( | |
| "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which " | |
| "should be the text to classify, and `labels`, which should be the list of labels to use for classification. " | |
| "It returns the most likely label in the list of provided `labels` for the input text." | |
| ) | |
| name = "text_classifier" | |
| pre_processor_class = AutoTokenizer | |
| model_class = AutoModelForSequenceClassification | |
| inputs = ["text", ["text"]] | |
| outputs = ["text"] | |
| def setup(self): | |
| super().setup() | |
| config = self.model.config | |
| self.entailment_id = -1 | |
| for idx, label in config.id2label.items(): | |
| if label.lower().startswith("entail"): | |
| self.entailment_id = int(idx) | |
| if self.entailment_id == -1: | |
| raise ValueError("Could not determine the entailment ID from the model config, please pass it at init.") | |
| def encode(self, text, labels): | |
| self._labels = labels | |
| return self.pre_processor( | |
| [text] * len(labels), | |
| [f"This example is {label}" for label in labels], | |
| return_tensors="pt", | |
| padding="max_length", | |
| ) | |
| def decode(self, outputs): | |
| logits = outputs.logits | |
| label_id = torch.argmax(logits[:, 2]).item() | |
| return self._labels[label_id] | |