#!/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 token classification. """ # You can also adapt this script on your own token classification task and datasets. Pointers for this are left as # comments. import logging import os import sys from dataclasses import dataclass, field from typing import Optional import datasets import evaluate import numpy as np from datasets import ClassLabel, load_dataset import transformers from transformers import ( AutoConfig, AutoModelForTokenClassification, AutoTokenizer, DataCollatorForTokenClassification, HfArgumentParser, PretrainedConfig, PreTrainedTokenizerFast, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.28.0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt") 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"} ) 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)." ) }, ) ignore_mismatched_sizes: bool = field( default=False, metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ task_name: Optional[str] = field(default="ner", metadata={"help": "The name of the task (ner, pos...)."}) 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 csv or JSON file)."} ) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate on (a csv or JSON file)."}, ) test_file: Optional[str] = field( default=None, metadata={"help": "An optional input test data file to predict on (a csv or JSON file)."}, ) text_column_name: Optional[str] = field( default=None, metadata={"help": "The column name of text to input in the file (a csv or JSON file)."} ) label_column_name: Optional[str] = field( default=None, metadata={"help": "The column name of label to input in the file (a csv or JSON 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=None, 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: 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." ) }, ) label_all_tokens: bool = field( default=False, metadata={ "help": ( "Whether to put the label for one word on all tokens of generated by that word or just on the " "one (in which case the other tokens will have a padding index)." ) }, ) return_entity_level_metrics: bool = field( default=False, metadata={"help": "Whether to return all the entity levels during evaluation or just the overall ones."}, ) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation 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." self.task_name = self.task_name.lower() 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, TrainingArguments)) 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() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_ner", model_args, data_args) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() log_level = training_args.get_process_log_level() logger.setLevel(log_level) datasets.utils.logging.set_verbosity(log_level) transformers.utils.logging.set_verbosity(log_level) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) logger.info(f"Training/evaluation parameters {training_args}") # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Set seed before initializing model. set_seed(training_args.seed) # 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. raw_datasets = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) else: data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file if data_args.test_file is not None: data_files["test"] = data_args.test_file extension = data_args.train_file.split(".")[-1] raw_datasets = load_dataset(extension, data_files=data_files, 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. if training_args.do_train: column_names = raw_datasets["train"].column_names features = raw_datasets["train"].features else: column_names = raw_datasets["validation"].column_names features = raw_datasets["validation"].features if data_args.text_column_name is not None: text_column_name = data_args.text_column_name elif "tokens" in column_names: text_column_name = "tokens" else: text_column_name = column_names[0] if data_args.label_column_name is not None: label_column_name = data_args.label_column_name elif f"{data_args.task_name}_tags" in column_names: label_column_name = f"{data_args.task_name}_tags" else: label_column_name = column_names[1] # In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the # unique labels. def get_label_list(labels): unique_labels = set() for label in labels: unique_labels = unique_labels | set(label) label_list = list(unique_labels) label_list.sort() return label_list # If the labels are of type ClassLabel, they are already integers and we have the map stored somewhere. # Otherwise, we have to get the list of labels manually. labels_are_int = isinstance(features[label_column_name].feature, ClassLabel) if labels_are_int: label_list = features[label_column_name].feature.names label_to_id = {i: i for i in range(len(label_list))} else: label_list = get_label_list(raw_datasets["train"][label_column_name]) label_to_id = {l: i for i, l in enumerate(label_list)} num_labels = len(label_list) # 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, num_labels=num_labels, finetuning_task=data_args.task_name, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) tokenizer_name_or_path = model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path if config.model_type in {"bloom", "gpt2", "roberta"}: tokenizer = AutoTokenizer.from_pretrained( tokenizer_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, add_prefix_space=True, ) else: tokenizer = AutoTokenizer.from_pretrained( tokenizer_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, ) model = AutoModelForTokenClassification.from_pretrained( model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_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, ignore_mismatched_sizes=model_args.ignore_mismatched_sizes, ) # 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" ) # Model has labels -> use them. if model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id: if sorted(model.config.label2id.keys()) == sorted(label_list): # Reorganize `label_list` to match the ordering of the model. if labels_are_int: label_to_id = {i: int(model.config.label2id[l]) for i, l in enumerate(label_list)} label_list = [model.config.id2label[i] for i in range(num_labels)] else: label_list = [model.config.id2label[i] for i in range(num_labels)] label_to_id = {l: i for i, l in enumerate(label_list)} else: logger.warning( "Your model seems to have been trained with labels, but they don't match the dataset: ", f"model labels: {sorted(model.config.label2id.keys())}, dataset labels:" f" {sorted(label_list)}.\nIgnoring the model labels as a result.", ) # Set the correspondences label/ID inside the model config model.config.label2id = {l: i for i, l in enumerate(label_list)} model.config.id2label = dict(enumerate(label_list)) # Map that sends B-Xxx label to its I-Xxx counterpart b_to_i_label = [] for idx, label in enumerate(label_list): if label.startswith("B-") and label.replace("B-", "I-") in label_list: b_to_i_label.append(label_list.index(label.replace("B-", "I-"))) else: b_to_i_label.append(idx) # Preprocessing the dataset # Padding strategy padding = "max_length" if data_args.pad_to_max_length else False # Tokenize all texts and align the labels with them. def tokenize_and_align_labels(examples): tokenized_inputs = tokenizer( examples[text_column_name], padding=padding, truncation=True, max_length=data_args.max_seq_length, # We use this argument because the texts in our dataset are lists of words (with a label for each word). is_split_into_words=True, ) labels = [] for i, label in enumerate(examples[label_column_name]): word_ids = tokenized_inputs.word_ids(batch_index=i) previous_word_idx = None label_ids = [] for word_idx in word_ids: # Special tokens have a word id that is None. We set the label to -100 so they are automatically # ignored in the loss function. if word_idx is None: label_ids.append(-100) # We set the label for the first token of each word. elif word_idx != previous_word_idx: label_ids.append(label_to_id[label[word_idx]]) # For the other tokens in a word, we set the label to either the current label or -100, depending on # the label_all_tokens flag. else: if data_args.label_all_tokens: label_ids.append(b_to_i_label[label_to_id[label[word_idx]]]) else: label_ids.append(-100) previous_word_idx = word_idx labels.append(label_ids) tokenized_inputs["labels"] = labels return tokenized_inputs if training_args.do_train: if "train" not in raw_datasets: raise ValueError("--do_train requires a train dataset") train_dataset = raw_datasets["train"] if data_args.max_train_samples is not None: max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(range(max_train_samples)) with training_args.main_process_first(desc="train dataset map pre-processing"): train_dataset = train_dataset.map( tokenize_and_align_labels, batched=True, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on train dataset", ) if training_args.do_eval: if "validation" not in raw_datasets: raise ValueError("--do_eval requires a validation dataset") eval_dataset = raw_datasets["validation"] if data_args.max_eval_samples is not None: max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) eval_dataset = eval_dataset.select(range(max_eval_samples)) with training_args.main_process_first(desc="validation dataset map pre-processing"): eval_dataset = eval_dataset.map( tokenize_and_align_labels, batched=True, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on validation dataset", ) if training_args.do_predict: if "test" not in raw_datasets: raise ValueError("--do_predict requires a test dataset") predict_dataset = raw_datasets["test"] if data_args.max_predict_samples is not None: max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples) predict_dataset = predict_dataset.select(range(max_predict_samples)) with training_args.main_process_first(desc="prediction dataset map pre-processing"): predict_dataset = predict_dataset.map( tokenize_and_align_labels, batched=True, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on prediction dataset", ) # Data collator data_collator = DataCollatorForTokenClassification(tokenizer, pad_to_multiple_of=8 if training_args.fp16 else None) # Metrics metric = evaluate.load("seqeval") def compute_metrics(p): predictions, labels = p predictions = np.argmax(predictions, axis=2) # Remove ignored index (special tokens) true_predictions = [ [label_list[p] for (p, l) in zip(prediction, label) if l != -100] for prediction, label in zip(predictions, labels) ] true_labels = [ [label_list[l] for (p, l) in zip(prediction, label) if l != -100] for prediction, label in zip(predictions, labels) ] results = metric.compute(predictions=true_predictions, references=true_labels) if data_args.return_entity_level_metrics: # Unpack nested dictionaries final_results = {} for key, value in results.items(): if isinstance(value, dict): for n, v in value.items(): final_results[f"{key}_{n}"] = v else: final_results[key] = value return final_results else: return { "precision": results["overall_precision"], "recall": results["overall_recall"], "f1": results["overall_f1"], "accuracy": results["overall_accuracy"], } # Initialize our Trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics, ) # Training if training_args.do_train: 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 trainer.save_model() # Saves the tokenizer too for easy upload max_train_samples = ( data_args.max_train_samples if data_args.max_train_samples is not None 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() max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset) metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset)) trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) # Predict if training_args.do_predict: logger.info("*** Predict ***") predictions, labels, metrics = trainer.predict(predict_dataset, metric_key_prefix="predict") predictions = np.argmax(predictions, axis=2) # Remove ignored index (special tokens) true_predictions = [ [label_list[p] for (p, l) in zip(prediction, label) if l != -100] for prediction, label in zip(predictions, labels) ] trainer.log_metrics("predict", metrics) trainer.save_metrics("predict", metrics) # Save predictions output_predictions_file = os.path.join(training_args.output_dir, "predictions.txt") if trainer.is_world_process_zero(): with open(output_predictions_file, "w") as writer: for prediction in true_predictions: writer.write(" ".join(prediction) + "\n") kwargs = {"finetuned_from": model_args.model_name_or_path, "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()