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Update Seq2Seq QA example script to use SQuAD metric. (#14335)
* Update postporcessing accordingly to use SQuAD metric. * Update assets accordingly based on SQuAD metrics. * Fix function naming error.
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examples/pytorch/question-answering/trainer_seq2seq_qa.py
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# coding=utf-8 | ||
# Copyright 2021 The HuggingFace 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. | ||
""" | ||
A subclass of `Trainer` specific to Question-Answering tasks | ||
""" | ||
from typing import Dict, List, Optional | ||
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from torch.utils.data import Dataset | ||
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from transformers import Seq2SeqTrainer, is_torch_tpu_available | ||
from transformers.trainer_utils import PredictionOutput | ||
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if is_torch_tpu_available(): | ||
import torch_xla.core.xla_model as xm | ||
import torch_xla.debug.metrics as met | ||
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class QuestionAnsweringSeq2SeqTrainer(Seq2SeqTrainer): | ||
def __init__(self, *args, eval_examples=None, post_process_function=None, **kwargs): | ||
super().__init__(*args, **kwargs) | ||
self.eval_examples = eval_examples | ||
self.post_process_function = post_process_function | ||
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# def evaluate(self, eval_dataset=None, eval_examples=None, ignore_keys=None, metric_key_prefix: str = "eval"): | ||
def evaluate( | ||
self, | ||
eval_dataset: Optional[Dataset] = None, | ||
eval_examples=None, | ||
ignore_keys: Optional[List[str]] = None, | ||
metric_key_prefix: str = "eval", | ||
max_length: Optional[int] = None, | ||
num_beams: Optional[int] = None, | ||
) -> Dict[str, float]: | ||
self._max_length = max_length if max_length is not None else self.args.generation_max_length | ||
self._num_beams = num_beams if num_beams is not None else self.args.generation_num_beams | ||
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eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset | ||
eval_dataloader = self.get_eval_dataloader(eval_dataset) | ||
eval_examples = self.eval_examples if eval_examples is None else eval_examples | ||
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# Temporarily disable metric computation, we will do it in the loop here. | ||
compute_metrics = self.compute_metrics | ||
self.compute_metrics = None | ||
eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop | ||
try: | ||
output = eval_loop( | ||
eval_dataloader, | ||
description="Evaluation", | ||
# No point gathering the predictions if there are no metrics, otherwise we defer to | ||
# self.args.prediction_loss_only | ||
prediction_loss_only=True if compute_metrics is None else None, | ||
ignore_keys=ignore_keys, | ||
) | ||
finally: | ||
self.compute_metrics = compute_metrics | ||
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if self.post_process_function is not None and self.compute_metrics is not None: | ||
eval_preds = self.post_process_function(eval_examples, eval_dataset, output) | ||
metrics = self.compute_metrics(eval_preds) | ||
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# Prefix all keys with metric_key_prefix + '_' | ||
for key in list(metrics.keys()): | ||
if not key.startswith(f"{metric_key_prefix}_"): | ||
metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key) | ||
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self.log(metrics) | ||
else: | ||
metrics = {} | ||
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if self.args.tpu_metrics_debug or self.args.debug: | ||
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.) | ||
xm.master_print(met.metrics_report()) | ||
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self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, metrics) | ||
return metrics | ||
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def predict(self, predict_dataset, predict_examples, ignore_keys=None, metric_key_prefix: str = "test"): | ||
predict_dataloader = self.get_test_dataloader(predict_dataset) | ||
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# Temporarily disable metric computation, we will do it in the loop here. | ||
compute_metrics = self.compute_metrics | ||
self.compute_metrics = None | ||
eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop | ||
try: | ||
output = eval_loop( | ||
predict_dataloader, | ||
description="Prediction", | ||
# No point gathering the predictions if there are no metrics, otherwise we defer to | ||
# self.args.prediction_loss_only | ||
prediction_loss_only=True if compute_metrics is None else None, | ||
ignore_keys=ignore_keys, | ||
) | ||
finally: | ||
self.compute_metrics = compute_metrics | ||
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if self.post_process_function is None or self.compute_metrics is None: | ||
return output | ||
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predictions = self.post_process_function(predict_examples, predict_dataset, output.predictions, "predict") | ||
metrics = self.compute_metrics(predictions) | ||
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# Prefix all keys with metric_key_prefix + '_' | ||
for key in list(metrics.keys()): | ||
if not key.startswith(f"{metric_key_prefix}_"): | ||
metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key) | ||
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return PredictionOutput(predictions=predictions.predictions, label_ids=predictions.label_ids, metrics=metrics) |
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