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Source code for flash.text.classification.model

# Copyright The PyTorch Lightning team.
#
# 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
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import os
import warnings
from typing import Any, Dict, List, Optional, Type, Union

from pytorch_lightning import Callback
from torch import Tensor

from flash.core.classification import ClassificationTask
from flash.core.data.io.input import DataKeys, ServeInput
from flash.core.data.io.input_transform import InputTransform
from flash.core.data.io.output import Output
from flash.core.registry import FlashRegistry
from flash.core.serve import Composition
from flash.core.utilities.imports import _TRANSFORMERS_AVAILABLE, requires
from flash.core.utilities.types import (
    INPUT_TRANSFORM_TYPE,
    LOSS_FN_TYPE,
    LR_SCHEDULER_TYPE,
    METRICS_TYPE,
    OPTIMIZER_TYPE,
)
from flash.text.classification.backbones import TEXT_CLASSIFIER_BACKBONES
from flash.text.classification.collate import TextClassificationCollate
from flash.text.input import TextDeserializer
from flash.text.ort_callback import ORTCallback

if _TRANSFORMERS_AVAILABLE:
    from transformers.modeling_outputs import Seq2SeqSequenceClassifierOutput, SequenceClassifierOutput


[docs]class TextClassifier(ClassificationTask): """The ``TextClassifier`` is a :class:`~flash.Task` for classifying text. For more details, see :ref:`text_classification`. The ``TextClassifier`` also supports multi-label classification with ``multi_label=True``. For more details, see :ref:`text_classification_multi_label`. Args: num_classes: Number of classes to classify. backbone: A model to use to compute text features can be any BERT model from HuggingFace/transformersimage. max_length: The maximum length to pad / truncate sequences to. optimizer: Optimizer to use for training. lr_scheduler: The LR scheduler to use during training. metrics: Metrics to compute for training and evaluation. Can either be an metric from the `torchmetrics` package, a custom metric inherenting from `torchmetrics.Metric`, a callable function or a list/dict containing a combination of the aforementioned. In all cases, each metric needs to have the signature `metric(preds,target)` and return a single scalar tensor. Defaults to :class:`torchmetrics.Accuracy`. learning_rate: Learning rate to use for training, defaults to `1e-3` multi_label: Whether the targets are multi-label or not. enable_ort: Enable Torch ONNX Runtime Optimization: https://onnxruntime.ai/docs/#onnx-runtime-for-training """ required_extras: str = "text" backbones: FlashRegistry = TEXT_CLASSIFIER_BACKBONES def __init__( self, num_classes: Optional[int] = None, labels: Optional[List[str]] = None, backbone: str = "prajjwal1/bert-medium", max_length: int = 128, loss_fn: LOSS_FN_TYPE = None, optimizer: OPTIMIZER_TYPE = "Adam", lr_scheduler: LR_SCHEDULER_TYPE = None, metrics: METRICS_TYPE = None, learning_rate: Optional[float] = None, multi_label: bool = False, enable_ort: bool = False, ): self.save_hyperparameters() if labels is not None and num_classes is None: num_classes = len(labels) os.environ["TOKENIZERS_PARALLELISM"] = "TRUE" # disable HF thousand warnings warnings.simplefilter("ignore") # set os environ variable for multiprocesses os.environ["PYTHONWARNINGS"] = "ignore" super().__init__( num_classes=num_classes, model=None, loss_fn=loss_fn, optimizer=optimizer, lr_scheduler=lr_scheduler, metrics=metrics, learning_rate=learning_rate, multi_label=multi_label, labels=labels, ) self.enable_ort = enable_ort self.max_length = max_length self.collate_fn = TextClassificationCollate(backbone=backbone, max_length=max_length) self.model = self.backbones.get(backbone)(num_labels=num_classes) @property def backbone(self): return self.model.base_model def forward(self, batch: Dict[str, Tensor]): result = self.model(input_ids=batch.get("input_ids", None), attention_mask=batch.get("attention_mask", None)) if isinstance(result, (SequenceClassifierOutput, Seq2SeqSequenceClassifierOutput)): result = result.logits return result def step(self, batch, batch_idx, metrics) -> dict: target = batch.pop(DataKeys.TARGET) batch = (batch, target) return super().step(batch, batch_idx, metrics) def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any: return self(batch) def _ci_benchmark_fn(self, history: List[Dict[str, Any]]): """This function is used only for debugging usage with CI.""" if self.hparams.multi_label: assert history[-1]["val_f1score"] > 0.40, history[-1]["val_f1score"] else: assert history[-1]["val_accuracy"] > 0.70, history[-1]["val_accuracy"] def configure_callbacks(self) -> List[Callback]: callbacks = super().configure_callbacks() or [] if self.enable_ort: callbacks.append(ORTCallback()) return callbacks @requires("serve") def serve( self, host: str = "127.0.0.1", port: int = 8000, sanity_check: bool = True, input_cls: Optional[Type[ServeInput]] = TextDeserializer, transform: INPUT_TRANSFORM_TYPE = InputTransform, transform_kwargs: Optional[Dict] = None, output: Optional[Union[str, Output]] = None, ) -> Composition: return super().serve(host, port, sanity_check, input_cls, transform, transform_kwargs, output)

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