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span-classification.py
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span-classification.py
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#!/usr/bin/env python3
# coding=utf-8
#
# 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 token classification.
"""
# You can also adapt this script on your own token classification task and datasets. Pointers for this are left as
# comments.
import os
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import evaluate
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorWithPadding,
HfArgumentParser,
PreTrainedTokenizerFast,
Trainer,
TrainingArguments,
EvalPrediction,
default_data_collator,
)
from utils import setup
from models.modeling_auto import AutoModelForSpanClassification
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_id: 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": "Where do you want 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 `huggingface-cli login` (necessary to use this script "
"with private models).")
},
)
hidden_dropout_prob: float = field(
default=0.1,
metadata={
"help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler."
})
save_model: bool = field(
default=False,
metadata={"help": ("Whether to save the model after training.")},
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
task_name: str = field(metadata={"help": "The name of the task (ner, pos...)."})
dataset_name: str = field(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)."})
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=512,
metadata={
"help": ("The maximum total input sequence length after tokenization. If set, 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 model maximum sentence length. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
"efficient on GPU but very bad for TPU.")
},
)
max_train_samples: int = field(
default=0,
metadata={
"help": ("For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set.")
},
)
# aggregate_all_tokens: bool = field(
# default=False,
# metadata={
# "help": ("Whether to pool all the tokens in the span instead of just taking the first.")
# },
# )
add_tokens: Optional[str] = field(default=None, metadata={"help": "Add special tokens to the vocabulary."})
def parse_columns(raw_datasets):
column_names = raw_datasets["train"].column_names
if "tokens1" in column_names:
text1_column_name, text2_column_name = "tokens1", "tokens2"
else:
text1_column_name, text2_column_name = "tokens", None
assert "index1" in column_names
index1_column_name, index2_column_name = "index1", "index2"
assert "label" in column_names
label_column_name = "label"
label_list = raw_datasets["train"].features[label_column_name].names
label2id = {l: i for i, l in enumerate(label_list)}
id2label = {i: l for i, l in enumerate(label_list)}
return text1_column_name, text2_column_name, index1_column_name, index2_column_name, label_column_name, label2id, id2label
def main():
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
logger, last_checkpoint, raw_datasets = setup(model_args, data_args, training_args)
text1_column_name, text2_column_name, index1_column_name, index2_column_name, label_column_name, label2id, id2label = parse_columns(
raw_datasets)
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_id,
id2label=id2label,
label2id=label2id,
finetuning_task=data_args.task_name,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
hidden_dropout_prob=model_args.hidden_dropout_prob,
output_hidden_states=False,
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_id,
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,
**({
"add_prefix_space": True
} if config.model_type in {"bloom", "gpt2", "roberta", "deberta"} else {}))
model = AutoModelForSpanClassification.from_pretrained(
model_args.model_id,
from_tf=bool(".ckpt" in model_args.model_id),
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,
)
assert isinstance(tokenizer, PreTrainedTokenizerFast), "This model does not have a Fast Tokenizer"
if data_args.add_tokens:
logger.info(f"Old vocabulary size: {len(tokenizer)}")
n = tokenizer.add_tokens(data_args.add_tokens.split())
model.resize_token_embeddings(len(tokenizer))
logger.info(str(data_args.add_tokens))
logger.info(f"Added {n} tokens. New vocabulary size: {len(tokenizer)}")
padding = "max_length" if data_args.pad_to_max_length else False
max_seq_length = tokenizer.model_max_length if data_args.max_seq_length is None else min(data_args.max_seq_length, tokenizer.model_max_length)
# assert data_args.aggregate_all_tokens is False, "not implemented"
# Tokenize all texts and align the labels with them.
def prepare_for_model(examples):
args = (
(examples[text1_column_name],) if text2_column_name is None else (examples[text1_column_name], examples[text2_column_name])
)
tokenized_inputs = tokenizer(
*args,
padding=padding,
truncation=True,
max_length=max_seq_length,
is_split_into_words=True,
)
# classifier_mask = []
# for i, input_ids in enumerate(tokenized_inputs["input_ids"]): # type: ignore
# span1 = tokenized_inputs.word_to_tokens(i, examples[index1_column_name][i], 0)
# span2 = tokenized_inputs.word_to_tokens(i, examples[index2_column_name][i], 1 if len(args) > 1 else 0)
# assert span1 is not None and span2 is not None, f"{span1} {span2}"
# start1 = span1.start
# start2 = span2.start
# mask = [1 if j in (start1, start2) else 0 for j in range(len(input_ids))]
# classifier_mask.append(mask)
# tokenized_inputs["classifier_mask"] = classifier_mask
return tokenized_inputs
def prepare_dataset(split):
if split not in raw_datasets:
raise ValueError(f"requires a {split} dataset")
split_dataset = raw_datasets[split]
if split == "train" and data_args.max_train_samples > 0:
max_train_samples = min(len(split_dataset), data_args.max_train_samples)
split_dataset = split_dataset.select(range(max_train_samples))
with training_args.main_process_first(desc=f"{split} dataset map pre-processing"):
split_dataset = split_dataset.map(
prepare_for_model,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
desc=f"Running tokenizer on {split} dataset",
)
return split_dataset
train_dataset = prepare_dataset("train") if training_args.do_train else None
eval_dataset = prepare_dataset("validation") if training_args.do_eval else None
predict_dataset = prepare_dataset("test") if training_args.do_predict else None
# Data collator
if data_args.pad_to_max_length:
data_collator = default_data_collator
elif training_args.fp16:
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
else:
data_collator = None
metric = evaluate.load("accuracy")
def compute_metrics(p: EvalPrediction):
preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
preds = np.argmax(preds, axis=1)
return metric.compute(predictions=preds, references=p.label_ids)
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset, # type: ignore
eval_dataset=eval_dataset, # type: ignore
tokenizer=tokenizer,
data_collator=data_collator,
compute_metrics=compute_metrics, # type: ignore
)
# Training
if training_args.do_train:
assert train_dataset is not None
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
if model_args.save_model:
trainer.save_model()
max_train_samples = (data_args.max_train_samples if data_args.max_train_samples > 0 else len(train_dataset))
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Predict
if training_args.do_predict:
logger.info("*** Predict ***")
predictions, _, metrics = trainer.predict(predict_dataset, metric_key_prefix="predict") # type: ignore
predictions = np.argmax(predictions, axis=1)
trainer.log_metrics("predict", metrics)
trainer.save_metrics("predict", metrics)
# Save predictions
output_predict_file = os.path.join(training_args.output_dir, f"predictions.txt")
if trainer.is_world_process_zero():
with open(output_predict_file, "w") as f:
for prediction in predictions:
if id2label is not None:
f.write(f"{id2label[prediction]}\n")
else:
f.write(f"{prediction}\n")
kwargs = {"finetuned_from": model_args.model_id, "tasks": "token-classification"}
if data_args.dataset_name is not None:
kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:
kwargs["dataset_args"] = data_args.dataset_config_name
kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
else:
kwargs["dataset"] = data_args.dataset_name
if training_args.push_to_hub:
trainer.push_to_hub(**kwargs)
else:
trainer.create_model_card(**kwargs)
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()