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Tensorflow QA example (#12252)

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* New Tensorflow QA example!

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* Update examples/tensorflow/question-answering/README.md

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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<!---
Copyright 2020 The HuggingFace Team. All rights reserved.
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.
@ -14,21 +14,42 @@ See the License for the specific language governing permissions and
limitations under the License.
-->
## SQuAD with the Tensorflow Trainer
# Question answering example
```bash
python run_tf_squad.py \
--model_name_or_path bert-base-uncased \
--output_dir model \
--max_seq_length 384 \
--num_train_epochs 2 \
--per_gpu_train_batch_size 8 \
--per_gpu_eval_batch_size 16 \
--do_train \
--logging_dir logs \
--logging_steps 10 \
--learning_rate 3e-5 \
--doc_stride 128
This folder contains the `run_qa.py` script, demonstrating *question answering* with the 🤗 Transformers library.
For straightforward use-cases you may be able to use this script without modification, although we have also
included comments in the code to indicate areas that you may need to adapt to your own projects.
### Usage notes
Note that when contexts are long they may be split into multiple training cases, not all of which may contain
the answer span.
As-is, the example script will train on SQuAD or any other question-answering dataset formatted the same way, and can handle user
inputs as well.
### Multi-GPU and TPU usage
By default, the script uses a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs
can also be used by passing the name of the TPU resource with the `--tpu` argument. There are some issues surrounding
these strategies and our models right now, which are most likely to appear in the evaluation/prediction steps. We're
actively working on better support for multi-GPU and TPU training in TF, but if you encounter problems a quick
workaround is to train in the multi-GPU or TPU context and then perform predictions outside of it.
### Memory usage and data loading
One thing to note is that all data is loaded into memory in this script. Most question answering datasets are small
enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle
data streaming. This is particularly challenging for TPUs, given the stricter requirements and the sheer volume of data
required to keep them fed. A full explanation of all the possible pitfalls is a bit beyond this example script and
README, but for more information you can see the 'Input Datasets' section of
[this document](https://www.tensorflow.org/guide/tpu).
### Example command
```
python run_qa.py \
--model_name_or_path distilbert-base-cased \
--output_dir output \
--dataset_name squad \
--do_train \
--do_eval \
```
For the moment evaluation is not available in the Tensorflow Trainer only the training.

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#!/usr/bin/env python
# coding=utf-8
# Copyright 2020 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.
"""
Fine-tuning the library models for question answering.
"""
# You can also adapt this script on your own question answering task. Pointers for this are left as comments.
import logging
import os
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional
import tensorflow as tf
from datasets import load_dataset, load_metric
import transformers
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizerFast,
TFAutoModelForQuestionAnswering,
TFTrainingArguments,
set_seed,
)
from transformers.file_utils import CONFIG_NAME, TF2_WEIGHTS_NAME
from transformers.utils import check_min_version
from utils_qa import postprocess_qa_predictions
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.7.0.dev0")
logger = logging.getLogger(__name__)
# region Arguments
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Path to directory to store the pretrained models downloaded from huggingface.co"},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
"with private models)."
},
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
test_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input test data file to evaluate the perplexity on (a text file)."},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
max_seq_length: int = field(
default=384,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
pad_to_max_length: bool = field(
default=False,
metadata={
"help": "Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch (which can "
"be faster on GPU but will be slower on TPU)."
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
},
)
version_2_with_negative: bool = field(
default=False, metadata={"help": "If true, some of the examples do not have an answer."}
)
null_score_diff_threshold: float = field(
default=0.0,
metadata={
"help": "The threshold used to select the null answer: if the best answer has a score that is less than "
"the score of the null answer minus this threshold, the null answer is selected for this example. "
"Only useful when `version_2_with_negative=True`."
},
)
doc_stride: int = field(
default=128,
metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
)
n_best_size: int = field(
default=20,
metadata={"help": "The total number of n-best predictions to generate when looking for an answer."},
)
max_answer_length: int = field(
default=30,
metadata={
"help": "The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another."
},
)
def __post_init__(self):
if (
self.dataset_name is None
and self.train_file is None
and self.validation_file is None
and self.test_file is None
):
raise ValueError("Need either a dataset name or a training/validation file/test_file.")
else:
if self.train_file is not None:
extension = self.train_file.split(".")[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
if self.test_file is not None:
extension = self.test_file.split(".")[-1]
assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."
# endregion
# region Helper classes
class SavePretrainedCallback(tf.keras.callbacks.Callback):
# Hugging Face models have a save_pretrained() method that saves both the weights and the necessary
# metadata to allow them to be loaded as a pretrained model in future. This is a simple Keras callback
# that saves the model with this method after each epoch.
def __init__(self, output_dir, **kwargs):
super().__init__()
self.output_dir = output_dir
def on_epoch_end(self, epoch, logs=None):
self.model.save_pretrained(self.output_dir)
def convert_dataset_for_tensorflow(
dataset, batch_size, dataset_mode="variable_batch", shuffle=True, drop_remainder=True
):
"""Converts a Hugging Face dataset to a Tensorflow Dataset. The dataset_mode controls whether we pad all batches
to the maximum sequence length, or whether we only pad to the maximum length within that batch. The former
is most useful when training on TPU, as a new graph compilation is required for each sequence length.
"""
def densify_ragged_batch(features, label=None):
features = {
feature: ragged_tensor.to_tensor(shape=batch_shape[feature]) if feature in tensor_keys else ragged_tensor
for feature, ragged_tensor in features.items()
}
if label is None:
return features
else:
return features, label
tensor_keys = ["attention_mask", "input_ids"]
label_keys = ["start_positions", "end_positions"]
if dataset_mode == "variable_batch":
batch_shape = {key: None for key in tensor_keys}
data = {key: tf.ragged.constant(dataset[key]) for key in tensor_keys}
elif dataset_mode == "constant_batch":
data = {key: tf.ragged.constant(dataset[key]) for key in tensor_keys}
batch_shape = {
key: tf.concat(([batch_size], ragged_tensor.bounding_shape()[1:]), axis=0)
for key, ragged_tensor in data.items()
}
else:
raise ValueError("Unknown dataset mode!")
if all([key in dataset.features for key in label_keys]):
for key in label_keys:
data[key] = tf.convert_to_tensor(dataset[key])
dummy_labels = tf.zeros_like(dataset[key])
tf_dataset = tf.data.Dataset.from_tensor_slices((data, dummy_labels))
else:
tf_dataset = tf.data.Dataset.from_tensor_slices(data)
if shuffle:
tf_dataset = tf_dataset.shuffle(buffer_size=len(dataset))
tf_dataset = tf_dataset.batch(batch_size=batch_size, drop_remainder=drop_remainder).map(densify_ragged_batch)
return tf_dataset
# endregion
def main():
# region Argument parsing
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
output_dir = Path(training_args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# endregion
# region Checkpoints
checkpoint = None
if len(os.listdir(training_args.output_dir)) > 0 and not training_args.overwrite_output_dir:
if (output_dir / CONFIG_NAME).is_file() and (output_dir / TF2_WEIGHTS_NAME).is_file():
checkpoint = output_dir
logger.info(
f"Checkpoint detected, resuming training from checkpoint in {training_args.output_dir}. To avoid this"
" behavior, change the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
else:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to continue regardless."
)
# endregion
# region Logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
logger.setLevel(logging.INFO if training_args.should_log else logging.WARN)
# Set the verbosity to info of the Transformers logger (on main process only):
if training_args.should_log:
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info(f"Training/evaluation parameters {training_args}")
# endregion
# Set seed before initializing model.
set_seed(training_args.seed)
# region Load Data
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
else:
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
extension = data_args.train_file.split(".")[-1]
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.validation_file.split(".")[-1]
if data_args.test_file is not None:
data_files["test"] = data_args.test_file
extension = data_args.test_file.split(".")[-1]
datasets = load_dataset(extension, data_files=data_files, field="data", cache_dir=model_args.cache_dir)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# endregion
# region Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=True,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
# endregion
# region Tokenizer check: this script requires a fast tokenizer.
if not isinstance(tokenizer, PreTrainedTokenizerFast):
raise ValueError(
"This example script only works for models that have a fast tokenizer. Checkout the big table of models "
"at https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet this "
"requirement"
)
# endregion
# region Preprocessing the datasets
# Preprocessing is slightly different for training and evaluation.
if training_args.do_train:
column_names = datasets["train"].column_names
elif training_args.do_eval:
column_names = datasets["validation"].column_names
else:
column_names = datasets["test"].column_names
question_column_name = "question" if "question" in column_names else column_names[0]
context_column_name = "context" if "context" in column_names else column_names[1]
answer_column_name = "answers" if "answers" in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
pad_on_right = tokenizer.padding_side == "right"
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
)
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
# Training preprocessing
def prepare_train_features(examples):
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
tokenized_examples = tokenizer(
examples[question_column_name if pad_on_right else context_column_name],
examples[context_column_name if pad_on_right else question_column_name],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_seq_length,
stride=data_args.doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length" if data_args.pad_to_max_length else False,
)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# The offset mappings will give us a map from token to character position in the original context. This will
# help us compute the start_positions and end_positions.
offset_mapping = tokenized_examples.pop("offset_mapping")
# Let's label those examples!
tokenized_examples["start_positions"] = []
tokenized_examples["end_positions"] = []
for i, offsets in enumerate(offset_mapping):
# We will label impossible answers with the index of the CLS token.
input_ids = tokenized_examples["input_ids"][i]
cls_index = input_ids.index(tokenizer.cls_token_id)
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples.sequence_ids(i)
# One example can give several spans, this is the index of the example containing this span of text.
sample_index = sample_mapping[i]
answers = examples[answer_column_name][sample_index]
# If no answers are given, set the cls_index as answer.
if len(answers["answer_start"]) == 0:
tokenized_examples["start_positions"].append(cls_index)
tokenized_examples["end_positions"].append(cls_index)
else:
# Start/end character index of the answer in the text.
start_char = answers["answer_start"][0]
end_char = start_char + len(answers["text"][0])
# Start token index of the current span in the text.
token_start_index = 0
while sequence_ids[token_start_index] != (1 if pad_on_right else 0):
token_start_index += 1
# End token index of the current span in the text.
token_end_index = len(input_ids) - 1
while sequence_ids[token_end_index] != (1 if pad_on_right else 0):
token_end_index -= 1
# Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):
tokenized_examples["start_positions"].append(cls_index)
tokenized_examples["end_positions"].append(cls_index)
else:
# Otherwise move the token_start_index and token_end_index to the two ends of the answer.
# Note: we could go after the last offset if the answer is the last word (edge case).
while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:
token_start_index += 1
tokenized_examples["start_positions"].append(token_start_index - 1)
while offsets[token_end_index][1] >= end_char:
token_end_index -= 1
tokenized_examples["end_positions"].append(token_end_index + 1)
return tokenized_examples
processed_datasets = dict()
if training_args.do_train:
if "train" not in datasets:
raise ValueError("--do_train requires a train dataset")
train_dataset = datasets["train"]
if data_args.max_train_samples is not None:
# We will select sample from whole data if agument is specified
train_dataset = train_dataset.select(range(data_args.max_train_samples))
# Create train feature from dataset
train_dataset = train_dataset.map(
prepare_train_features,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
)
if data_args.max_train_samples is not None:
# Number of samples might increase during Feature Creation, We select only specified max samples
train_dataset = train_dataset.select(range(data_args.max_train_samples))
processed_datasets["train"] = train_dataset
# Validation preprocessing
def prepare_validation_features(examples):
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
tokenized_examples = tokenizer(
examples[question_column_name if pad_on_right else context_column_name],
examples[context_column_name if pad_on_right else question_column_name],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_seq_length,
stride=data_args.doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length" if data_args.pad_to_max_length else False,
)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
tokenized_examples["example_id"] = []
for i in range(len(tokenized_examples["input_ids"])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
tokenized_examples["offset_mapping"][i] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
if training_args.do_eval:
if "validation" not in datasets:
raise ValueError("--do_eval requires a validation dataset")
eval_examples = datasets["validation"]
if data_args.max_eval_samples is not None:
# We will select sample from whole data
eval_examples = eval_examples.select(range(data_args.max_eval_samples))
# Validation Feature Creation
eval_dataset = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
)
if data_args.max_eval_samples is not None:
# During Feature creation dataset samples might increase, we will select required samples again
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
processed_datasets["validation"] = eval_dataset
if training_args.do_predict:
if "test" not in datasets:
raise ValueError("--do_predict requires a test dataset")
predict_examples = datasets["test"]
if data_args.max_predict_samples is not None:
# We will select sample from whole data
predict_examples = predict_examples.select(range(data_args.max_predict_samples))
# Predict Feature Creation
predict_dataset = predict_examples.map(
prepare_validation_features,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
)
if data_args.max_predict_samples is not None:
# During Feature creation dataset samples might increase, we will select required samples again
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
processed_datasets["test"] = predict_dataset
# endregion
# region Metrics and Post-processing:
def post_processing_function(examples, features, predictions, stage="eval"):
# Post-processing: we match the start logits and end logits to answers in the original context.
predictions = postprocess_qa_predictions(
examples=examples,
features=features,
predictions=predictions,
version_2_with_negative=data_args.version_2_with_negative,
n_best_size=data_args.n_best_size,
max_answer_length=data_args.max_answer_length,
null_score_diff_threshold=data_args.null_score_diff_threshold,
output_dir=training_args.output_dir,
prefix=stage,
)
# Format the result to the format the metric expects.
if data_args.version_2_with_negative:
formatted_predictions = [
{"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
]
else:
formatted_predictions = [{"id": k, "prediction_text": v} for k, v in predictions.items()]
references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=formatted_predictions, label_ids=references)
metric = load_metric("squad_v2" if data_args.version_2_with_negative else "squad")
def compute_metrics(p: EvalPrediction):
return metric.compute(predictions=p.predictions, references=p.label_ids)
# endregion
with training_args.strategy.scope():
# region Load model
if checkpoint is None:
model_path = model_args.model_name_or_path
else:
model_path = checkpoint
model = TFAutoModelForQuestionAnswering.from_pretrained(
model_path,
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
)
optimizer = tf.keras.optimizers.Adam(
learning_rate=training_args.learning_rate,
beta_1=training_args.adam_beta1,
beta_2=training_args.adam_beta2,
epsilon=training_args.adam_epsilon,
clipnorm=training_args.max_grad_norm,
)
def dummy_loss(y_true, y_pred):
return tf.reduce_mean(y_pred)
losses = {"loss": dummy_loss}
model.compile(optimizer=optimizer, loss=losses)
# endregion
# region Training
if training_args.do_train:
# Make a tf.data.Dataset for this
if isinstance(training_args.strategy, tf.distribute.TPUStrategy) or data_args.pad_to_max_length:
logger.info("Padding all batches to max length because argument was set or we're on TPU.")
dataset_mode = "constant_batch"
else:
dataset_mode = "variable_batch"
training_dataset = convert_dataset_for_tensorflow(
processed_datasets["train"],
batch_size=training_args.per_device_train_batch_size,
dataset_mode=dataset_mode,
drop_remainder=True,
shuffle=True,
)
model.fit(training_dataset, epochs=int(training_args.num_train_epochs))
# endregion
# region Evaluation
if training_args.do_eval:
logger.info("*** Evaluation ***")
eval_inputs = {
"input_ids": tf.ragged.constant(processed_datasets["validation"]["input_ids"]).to_tensor(),
"attention_mask": tf.ragged.constant(processed_datasets["validation"]["attention_mask"]).to_tensor(),
}
eval_predictions = model.predict(eval_inputs)
post_processed_eval = post_processing_function(
datasets["validation"],
processed_datasets["validation"],
(eval_predictions.start_logits, eval_predictions.end_logits),
)
metrics = compute_metrics(post_processed_eval)
logging.info("Evaluation metrics:")
for metric, value in metrics.items():
logging.info(f"{metric}: {value:.3f}")
# endregion
# region Prediction
if training_args.do_predict:
logger.info("*** Predict ***")
predict_inputs = {
"input_ids": tf.ragged.constant(processed_datasets["test"]["input_ids"]).to_tensor(),
"attention_mask": tf.ragged.constant(processed_datasets["test"]["attention_mask"]).to_tensor(),
}
test_predictions = model.predict(predict_inputs)
post_processed_test = post_processing_function(
datasets["test"],
processed_datasets["test"],
(test_predictions.start_logits, test_predictions.end_logits),
)
metrics = compute_metrics(post_processed_test)
logging.info("Test metrics:")
for metric, value in metrics.items():
logging.info(f"{metric}: {value:.3f}")
# endregion
if training_args.push_to_hub:
model.push_to_hub()
if __name__ == "__main__":
main()

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examples/tensorflow/question-answering/run_tf_squad.py
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@ -1,255 +0,0 @@
#!/usr/bin/env python
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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.
""" Fine-tuning the library models for question-answering."""
import logging
import os
from dataclasses import dataclass, field
from typing import Optional
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
HfArgumentParser,
TFAutoModelForQuestionAnswering,
TFTrainer,
TFTrainingArguments,
squad_convert_examples_to_features,
)
from transformers.data.processors.squad import SquadV1Processor, SquadV2Processor
from transformers.utils import logging as hf_logging
hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
logger = logging.getLogger(__name__)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
use_fast: bool = field(default=False, metadata={"help": "Set this flag to use fast tokenization."})
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
data_dir: Optional[str] = field(
default=None, metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."}
)
use_tfds: Optional[bool] = field(default=True, metadata={"help": "If TFDS should be used or not."})
max_seq_length: int = field(
default=128,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
doc_stride: int = field(
default=128,
metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
)
max_query_length: int = field(
default=64,
metadata={
"help": "The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length."
},
)
max_answer_length: int = field(
default=30,
metadata={
"help": "The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another."
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
version_2_with_negative: bool = field(
default=False, metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."}
)
null_score_diff_threshold: float = field(
default=0.0, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
)
n_best_size: int = field(
default=20, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
)
lang_id: int = field(
default=0,
metadata={
"help": "language id of input for language-specific xlm models (see tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
},
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(
f"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1)}, "
f"16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Prepare Question-Answering task
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast,
)
with training_args.strategy.scope():
model = TFAutoModelForQuestionAnswering.from_pretrained(
model_args.model_name_or_path,
from_pt=bool(".bin" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
# Get datasets
if data_args.use_tfds:
if data_args.version_2_with_negative:
logger.warning("tensorflow_datasets does not handle version 2 of SQuAD. Switch to version 1 automatically")
try:
import tensorflow_datasets as tfds
except ImportError:
raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.")
tfds_examples = tfds.load("squad", data_dir=data_args.data_dir)
train_examples = (
SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=False)
if training_args.do_train
else None
)
eval_examples = (
SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=True)
if training_args.do_eval
else None
)
else:
processor = SquadV2Processor() if data_args.version_2_with_negative else SquadV1Processor()
train_examples = processor.get_train_examples(data_args.data_dir) if training_args.do_train else None
eval_examples = processor.get_dev_examples(data_args.data_dir) if training_args.do_eval else None
train_dataset = (
squad_convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=data_args.max_seq_length,
doc_stride=data_args.doc_stride,
max_query_length=data_args.max_query_length,
is_training=True,
return_dataset="tf",
)
if training_args.do_train
else None
)
train_dataset = train_dataset.apply(tf.data.experimental.assert_cardinality(len(train_examples)))
eval_dataset = (
squad_convert_examples_to_features(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=data_args.max_seq_length,
doc_stride=data_args.doc_stride,
max_query_length=data_args.max_query_length,
is_training=False,
return_dataset="tf",
)
if training_args.do_eval
else None
)
eval_dataset = eval_dataset.apply(tf.data.experimental.assert_cardinality(len(eval_examples)))
# Initialize our Trainer
trainer = TFTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
)
# Training
if training_args.do_train:
trainer.train()
trainer.save_model()
tokenizer.save_pretrained(training_args.output_dir)
if __name__ == "__main__":
main()

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examples/tensorflow/question-answering/utils_qa.py Normal file
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@ -0,0 +1,425 @@
# coding=utf-8
# Copyright 2020 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.
"""
Post-processing utilities for question answering.
"""
import collections
import json
import logging
import os
from typing import Optional, Tuple
import numpy as np
from tqdm.auto import tqdm
logger = logging.getLogger(__name__)
def postprocess_qa_predictions(
examples,
features,
predictions: Tuple[np.ndarray, np.ndarray],
version_2_with_negative: bool = False,
n_best_size: int = 20,
max_answer_length: int = 30,
null_score_diff_threshold: float = 0.0,
output_dir: Optional[str] = None,
prefix: Optional[str] = None,
):
"""
Post-processes the predictions of a question-answering model to convert them to answers that are substrings of the
original contexts. This is the base postprocessing functions for models that only return start and end logits.
Args:
examples: The non-preprocessed dataset (see the main script for more information).
features: The processed dataset (see the main script for more information).
predictions (:obj:`Tuple[np.ndarray, np.ndarray]`):
The predictions of the model: two arrays containing the start logits and the end logits respectively. Its
first dimension must match the number of elements of :obj:`features`.
version_2_with_negative (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the underlying dataset contains examples with no answers.
n_best_size (:obj:`int`, `optional`, defaults to 20):
The total number of n-best predictions to generate when looking for an answer.
max_answer_length (:obj:`int`, `optional`, defaults to 30):
The maximum length of an answer that can be generated. This is needed because the start and end predictions
are not conditioned on one another.
null_score_diff_threshold (:obj:`float`, `optional`, defaults to 0):
The threshold used to select the null answer: if the best answer has a score that is less than the score of
the null answer minus this threshold, the null answer is selected for this example (note that the score of
the null answer for an example giving several features is the minimum of the scores for the null answer on
each feature: all features must be aligned on the fact they `want` to predict a null answer).
Only useful when :obj:`version_2_with_negative` is :obj:`True`.
output_dir (:obj:`str`, `optional`):
If provided, the dictionaries of predictions, n_best predictions (with their scores and logits) and, if
:obj:`version_2_with_negative=True`, the dictionary of the scores differences between best and null
answers, are saved in `output_dir`.
prefix (:obj:`str`, `optional`):
If provided, the dictionaries mentioned above are saved with `prefix` added to their names.
is_world_process_zero (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether this process is the main process or not (used to determine if logging/saves should be done).
"""
assert len(predictions) == 2, "`predictions` should be a tuple with two elements (start_logits, end_logits)."
all_start_logits, all_end_logits = predictions
assert len(predictions[0]) == len(features), f"Got {len(predictions[0])} predictions and {len(features)} features."
# Build a map example to its corresponding features.
example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
features_per_example = collections.defaultdict(list)
for i, feature in enumerate(features):
features_per_example[example_id_to_index[feature["example_id"]]].append(i)
# The dictionaries we have to fill.
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
if version_2_with_negative:
scores_diff_json = collections.OrderedDict()
# Logging.
logger.info(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")
# Let's loop over all the examples!
for example_index, example in enumerate(tqdm(examples)):
# Those are the indices of the features associated to the current example.
feature_indices = features_per_example[example_index]
min_null_prediction = None
prelim_predictions = []
# Looping through all the features associated to the current example.
for feature_index in feature_indices:
# We grab the predictions of the model for this feature.
start_logits = all_start_logits[feature_index]
end_logits = all_end_logits[feature_index]
# This is what will allow us to map some the positions in our logits to span of texts in the original
# context.
offset_mapping = features[feature_index]["offset_mapping"]
# Optional `token_is_max_context`, if provided we will remove answers that do not have the maximum context
# available in the current feature.
token_is_max_context = features[feature_index].get("token_is_max_context", None)
# Update minimum null prediction.
feature_null_score = start_logits[0] + end_logits[0]
if min_null_prediction is None or min_null_prediction["score"] > feature_null_score:
min_null_prediction = {
"offsets": (0, 0),
"score": feature_null_score,
"start_logit": start_logits[0],
"end_logit": end_logits[0],
}
# Go through all possibilities for the `n_best_size` greater start and end logits.
start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()
end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()
for start_index in start_indexes:
for end_index in end_indexes:
# Don't consider out-of-scope answers, either because the indices are out of bounds or correspond
# to part of the input_ids that are not in the context.
if (
start_index >= len(offset_mapping)
or end_index >= len(offset_mapping)
or offset_mapping[start_index] is None
or offset_mapping[end_index] is None
):
continue
# Don't consider answers with a length that is either < 0 or > max_answer_length.
if end_index < start_index or end_index - start_index + 1 > max_answer_length:
continue
# Don't consider answer that don't have the maximum context available (if such information is
# provided).
if token_is_max_context is not None and not token_is_max_context.get(str(start_index), False):
continue
prelim_predictions.append(
{
"offsets": (offset_mapping[start_index][0], offset_mapping[end_index][1]),
"score": start_logits[start_index] + end_logits[end_index],
"start_logit": start_logits[start_index],
"end_logit": end_logits[end_index],
}
)
if version_2_with_negative:
# Add the minimum null prediction
prelim_predictions.append(min_null_prediction)
null_score = min_null_prediction["score"]
# Only keep the best `n_best_size` predictions.
predictions = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[:n_best_size]
# Add back the minimum null prediction if it was removed because of its low score.
if version_2_with_negative and not any(p["offsets"] == (0, 0) for p in predictions):
predictions.append(min_null_prediction)
# Use the offsets to gather the answer text in the original context.
context = example["context"]
for pred in predictions:
offsets = pred.pop("offsets")
pred["text"] = context[offsets[0] : offsets[1]]
# In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
# failure.
if len(predictions) == 0 or (len(predictions) == 1 and predictions[0]["text"] == ""):
predictions.insert(0, {"text": "empty", "start_logit": 0.0, "end_logit": 0.0, "score": 0.0})
# Compute the softmax of all scores (we do it with numpy to stay independent from torch/tf in this file, using
# the LogSumExp trick).
scores = np.array([pred.pop("score") for pred in predictions])
exp_scores = np.exp(scores - np.max(scores))
probs = exp_scores / exp_scores.sum()
# Include the probabilities in our predictions.
for prob, pred in zip(probs, predictions):
pred["probability"] = prob
# Pick the best prediction. If the null answer is not possible, this is easy.
if not version_2_with_negative:
all_predictions[example["id"]] = predictions[0]["text"]
else:
# Otherwise we first need to find the best non-empty prediction.
i = 0
while predictions[i]["text"] == "":
i += 1
best_non_null_pred = predictions[i]
# Then we compare to the null prediction using the threshold.
score_diff = null_score - best_non_null_pred["start_logit"] - best_non_null_pred["end_logit"]
scores_diff_json[example["id"]] = float(score_diff) # To be JSON-serializable.
if score_diff > null_score_diff_threshold:
all_predictions[example["id"]] = ""
else:
all_predictions[example["id"]] = best_non_null_pred["text"]
# Make `predictions` JSON-serializable by casting np.float back to float.
all_nbest_json[example["id"]] = [
{k: (float(v) if isinstance(v, (np.float16, np.float32, np.float64)) else v) for k, v in pred.items()}
for pred in predictions
]
# If we have an output_dir, let's save all those dicts.
if output_dir is not None:
assert os.path.isdir(output_dir), f"{output_dir} is not a directory."
prediction_file = os.path.join(
output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
)
nbest_file = os.path.join(
output_dir, "nbest_predictions.json" if prefix is None else f"{prefix}_nbest_predictions.json"
)
if version_2_with_negative:
null_odds_file = os.path.join(
output_dir, "null_odds.json" if prefix is None else f"{prefix}_null_odds.json"
)
logger.info(f"Saving predictions to {prediction_file}.")
with open(prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
logger.info(f"Saving nbest_preds to {nbest_file}.")
with open(nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
logger.info(f"Saving null_odds to {null_odds_file}.")
with open(null_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions
def postprocess_qa_predictions_with_beam_search(
examples,
features,
predictions: Tuple[np.ndarray, np.ndarray],
version_2_with_negative: bool = False,
n_best_size: int = 20,
max_answer_length: int = 30,
start_n_top: int = 5,
end_n_top: int = 5,
output_dir: Optional[str] = None,
prefix: Optional[str] = None,
is_world_process_zero: bool = True,
):
"""
Post-processes the predictions of a question-answering model with beam search to convert them to answers that are substrings of the
original contexts. This is the postprocessing functions for models that return start and end logits, indices, as well as
cls token predictions.
Args:
examples: The non-preprocessed dataset (see the main script for more information).
features: The processed dataset (see the main script for more information).
predictions (:obj:`Tuple[np.ndarray, np.ndarray]`):
The predictions of the model: two arrays containing the start logits and the end logits respectively. Its
first dimension must match the number of elements of :obj:`features`.
version_2_with_negative (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the underlying dataset contains examples with no answers.
n_best_size (:obj:`int`, `optional`, defaults to 20):
The total number of n-best predictions to generate when looking for an answer.
max_answer_length (:obj:`int`, `optional`, defaults to 30):
The maximum length of an answer that can be generated. This is needed because the start and end predictions
are not conditioned on one another.
start_n_top (:obj:`int`, `optional`, defaults to 5):
The number of top start logits too keep when searching for the :obj:`n_best_size` predictions.
end_n_top (:obj:`int`, `optional`, defaults to 5):
The number of top end logits too keep when searching for the :obj:`n_best_size` predictions.
output_dir (:obj:`str`, `optional`):
If provided, the dictionaries of predictions, n_best predictions (with their scores and logits) and, if
:obj:`version_2_with_negative=True`, the dictionary of the scores differences between best and null
answers, are saved in `output_dir`.
prefix (:obj:`str`, `optional`):
If provided, the dictionaries mentioned above are saved with `prefix` added to their names.
is_world_process_zero (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether this process is the main process or not (used to determine if logging/saves should be done).
"""
assert len(predictions) == 5, "`predictions` should be a tuple with five elements."
start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits = predictions
assert len(predictions[0]) == len(
features
), f"Got {len(predictions[0])} predicitions and {len(features)} features."
# Build a map example to its corresponding features.
example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
features_per_example = collections.defaultdict(list)
for i, feature in enumerate(features):
features_per_example[example_id_to_index[feature["example_id"]]].append(i)
# The dictionaries we have to fill.
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict() if version_2_with_negative else None
# Logging.
logger.setLevel(logging.INFO if is_world_process_zero else logging.WARN)
logger.info(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")
# Let's loop over all the examples!
for example_index, example in enumerate(tqdm(examples)):
# Those are the indices of the features associated to the current example.
feature_indices = features_per_example[example_index]
min_null_score = None
prelim_predictions = []
# Looping through all the features associated to the current example.
for feature_index in feature_indices:
# We grab the predictions of the model for this feature.
start_log_prob = start_top_log_probs[feature_index]
start_indexes = start_top_index[feature_index]
end_log_prob = end_top_log_probs[feature_index]
end_indexes = end_top_index[feature_index]
feature_null_score = cls_logits[feature_index]
# This is what will allow us to map some the positions in our logits to span of texts in the original
# context.
offset_mapping = features[feature_index]["offset_mapping"]
# Optional `token_is_max_context`, if provided we will remove answers that do not have the maximum context
# available in the current feature.
token_is_max_context = features[feature_index].get("token_is_max_context", None)
# Update minimum null prediction
if min_null_score is None or feature_null_score < min_null_score:
min_null_score = feature_null_score
# Go through all possibilities for the `n_start_top`/`n_end_top` greater start and end logits.
for i in range(start_n_top):
for j in range(end_n_top):
start_index = int(start_indexes[i])
j_index = i * end_n_top + j
end_index = int(end_indexes[j_index])
# Don't consider out-of-scope answers (last part of the test should be unnecessary because of the
# p_mask but let's not take any risk)
if (
start_index >= len(offset_mapping)
or end_index >= len(offset_mapping)
or offset_mapping[start_index] is None
or offset_mapping[end_index] is None
):
continue
# Don't consider answers with a length negative or > max_answer_length.
if end_index < start_index or end_index - start_index + 1 > max_answer_length:
continue
# Don't consider answer that don't have the maximum context available (if such information is
# provided).
if token_is_max_context is not None and not token_is_max_context.get(str(start_index), False):
continue
prelim_predictions.append(
{
"offsets": (offset_mapping[start_index][0], offset_mapping[end_index][1]),
"score": start_log_prob[i] + end_log_prob[j_index],
"start_log_prob": start_log_prob[i],
"end_log_prob": end_log_prob[j_index],
}
)
# Only keep the best `n_best_size` predictions.
predictions = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[:n_best_size]
# Use the offsets to gather the answer text in the original context.
context = example["context"]
for pred in predictions:
offsets = pred.pop("offsets")
pred["text"] = context[offsets[0] : offsets[1]]
# In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
# failure.
if len(predictions) == 0:
predictions.insert(0, {"text": "", "start_logit": -1e-6, "end_logit": -1e-6, "score": -2e-6})
# Compute the softmax of all scores (we do it with numpy to stay independent from torch/tf in this file, using
# the LogSumExp trick).
scores = np.array([pred.pop("score") for pred in predictions])
exp_scores = np.exp(scores - np.max(scores))
probs = exp_scores / exp_scores.sum()
# Include the probabilities in our predictions.
for prob, pred in zip(probs, predictions):
pred["probability"] = prob
# Pick the best prediction and set the probability for the null answer.
all_predictions[example["id"]] = predictions[0]["text"]
if version_2_with_negative:
scores_diff_json[example["id"]] = float(min_null_score)
# Make `predictions` JSON-serializable by casting np.float back to float.
all_nbest_json[example["id"]] = [
{k: (float(v) if isinstance(v, (np.float16, np.float32, np.float64)) else v) for k, v in pred.items()}
for pred in predictions
]
# If we have an output_dir, let's save all those dicts.
if output_dir is not None:
assert os.path.isdir(output_dir), f"{output_dir} is not a directory."
prediction_file = os.path.join(
output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
)
nbest_file = os.path.join(
output_dir, "nbest_predictions.json" if prefix is None else f"{prefix}_nbest_predictions.json"
)
if version_2_with_negative:
null_odds_file = os.path.join(
output_dir, "null_odds.json" if prefix is None else f"{prefix}_null_odds.json"
)
print(f"Saving predictions to {prediction_file}.")
with open(prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
print(f"Saving nbest_preds to {nbest_file}.")
with open(nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
print(f"Saving null_odds to {null_odds_file}.")
with open(null_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions, scores_diff_json