# 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. """ Finetuning the library models for question-answering on SQuAD (DistilBERT, Bert, XLM, XLNet).""" from __future__ import absolute_import, division, print_function from transformers.data.processors.squad import SquadV1Processor, SquadV2Processor, SquadResult from transformers.data.metrics.squad_metrics import compute_predictions_logits, compute_predictions_log_probs, squad_evaluate import argparse import logging import os import random import glob import timeit import numpy as np import torch from torch.utils.data import ( DataLoader, RandomSampler, SequentialSampler, TensorDataset) from torch.utils.data.distributed import DistributedSampler try: from torch.utils.tensorboard import SummaryWriter except: from tensorboardX import SummaryWriter from tqdm import tqdm, trange from transformers import (WEIGHTS_NAME, BertConfig, BertForQuestionAnswering, BertTokenizer, XLMConfig, XLMForQuestionAnswering, XLMTokenizer, XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer, DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer, AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer, XLMConfig, XLMForQuestionAnswering, XLMTokenizer, ) from transformers import AdamW, get_linear_schedule_with_warmup, squad_convert_examples_to_features logger = logging.getLogger(__name__) ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLNetConfig, XLMConfig)), ()) MODEL_CLASSES = { 'bert': (BertConfig, BertForQuestionAnswering, BertTokenizer), 'xlnet': (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer), 'xlm': (XLMConfig, XLMForQuestionAnswering, XLMTokenizer), 'distilbert': (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer), 'albert': (AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer), 'xlm': (XLMConfig, XLMForQuestionAnswering, XLMTokenizer) } def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) def to_list(tensor): return tensor.detach().cpu().tolist() def train(args, train_dataset, model, tokenizer): """ Train the model """ if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = RandomSampler( train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader( train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) if args.max_steps > 0: t_total = args.max_steps args.num_train_epochs = args.max_steps // ( len(train_dataloader) // args.gradient_accumulation_steps) + 1 else: t_total = len( train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs # Prepare optimizer and schedule (linear warmup and decay) no_decay = ['bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [ {'params': [p for n, p in model.named_parameters() if not any( nd in n for nd in no_decay)], 'weight_decay': args.weight_decay}, {'params': [p for n, p in model.named_parameters() if any( nd in n for nd in no_decay)], 'weight_decay': 0.0} ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total) # Check if saved optimizer or scheduler states exist if os.path.isfile(os.path.join(args.model_name_or_path, 'optimizer.pt')) and os.path.isfile(os.path.join(args.model_name_or_path, 'scheduler.pt')): # Load in optimizer and scheduler states optimizer.load_state_dict(torch.load( os.path.join(args.model_name_or_path, 'optimizer.pt'))) scheduler.load_state_dict(torch.load( os.path.join(args.model_name_or_path, 'scheduler.pt'))) if args.fp16: try: from apex import amp except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize( model, optimizer, opt_level=args.fp16_opt_level) # multi-gpu training (should be after apex fp16 initialization) if args.n_gpu > 1: model = torch.nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if args.local_rank != -1: model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1)) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) global_step = 1 epochs_trained = 0 steps_trained_in_current_epoch = 0 # Check if continuing training from a checkpoint if os.path.exists(args.model_name_or_path): # set global_step to gobal_step of last saved checkpoint from model path global_step = int(args.model_name_or_path.split('-')[-1].split('/')[0]) epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps) steps_trained_in_current_epoch = global_step % ( len(train_dataloader) // args.gradient_accumulation_steps) logger.info( " Continuing training from checkpoint, will skip to saved global_step") logger.info(" Continuing training from epoch %d", epochs_trained) logger.info(" Continuing training from global step %d", global_step) logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch) tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange(epochs_trained, int( args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]) # Added here for reproductibility (even between python 2 and 3) set_seed(args) for _ in train_iterator: epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) for step, batch in enumerate(epoch_iterator): # Skip past any already trained steps if resuming training if steps_trained_in_current_epoch > 0: steps_trained_in_current_epoch -= 1 continue model.train() batch = tuple(t.to(args.device) for t in batch) inputs = { 'input_ids': batch[0], 'attention_mask': batch[1], 'start_positions': batch[3], 'end_positions': batch[4] } if args.model_type != 'distilbert': inputs['token_type_ids'] = None if args.model_type == 'xlm' else batch[2] if args.model_type in ['xlnet', 'xlm']: inputs.update({'cls_index': batch[5], 'p_mask': batch[6]}) outputs = model(**inputs) # model outputs are always tuple in transformers (see doc) loss = outputs[0] if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16: torch.nn.utils.clip_grad_norm_( amp.master_params(optimizer), args.max_grad_norm) else: torch.nn.utils.clip_grad_norm_( model.parameters(), args.max_grad_norm) optimizer.step() scheduler.step() # Update learning rate schedule model.zero_grad() global_step += 1 # Log metrics if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: # Only evaluate when single GPU otherwise metrics may not average well if args.local_rank == -1 and args.evaluate_during_training: results = evaluate(args, model, tokenizer) for key, value in results.items(): tb_writer.add_scalar( 'eval_{}'.format(key), value, global_step) tb_writer.add_scalar( 'lr', scheduler.get_lr()[0], global_step) tb_writer.add_scalar( 'loss', (tr_loss - logging_loss)/args.logging_steps, global_step) logging_loss = tr_loss # Save model checkpoint if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: output_dir = os.path.join( args.output_dir, 'checkpoint-{}'.format(global_step)) if not os.path.exists(output_dir): os.makedirs(output_dir) # Take care of distributed/parallel training model_to_save = model.module if hasattr( model, 'module') else model model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) torch.save(args, os.path.join( output_dir, 'training_args.bin')) logger.info("Saving model checkpoint to %s", output_dir) torch.save(optimizer.state_dict(), os.path.join( output_dir, 'optimizer.pt')) torch.save(scheduler.state_dict(), os.path.join( output_dir, 'scheduler.pt')) logger.info( "Saving optimizer and scheduler states to %s", output_dir) if args.max_steps > 0 and global_step > args.max_steps: epoch_iterator.close() break if args.max_steps > 0 and global_step > args.max_steps: train_iterator.close() break if args.local_rank in [-1, 0]: tb_writer.close() return global_step, tr_loss / global_step def evaluate(args, model, tokenizer, prefix=""): dataset, examples, features = load_and_cache_examples( args, tokenizer, evaluate=True, output_examples=True) if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(dataset) eval_dataloader = DataLoader( dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu evaluate if args.n_gpu > 1: model = torch.nn.DataParallel(model) # Eval! logger.info("***** Running evaluation {} *****".format(prefix)) logger.info(" Num examples = %d", len(dataset)) logger.info(" Batch size = %d", args.eval_batch_size) all_results = [] start_time = timeit.default_timer() for batch in tqdm(eval_dataloader, desc="Evaluating"): model.eval() batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = { 'input_ids': batch[0], 'attention_mask': batch[1] } if args.model_type != 'distilbert': # XLM don't use segment_ids inputs['token_type_ids'] = None if args.model_type == 'xlm' else batch[2] example_indices = batch[3] # XLNet and XLM use more arguments for their predictions if args.model_type in ['xlnet', 'xlm']: inputs.update({'cls_index': batch[4], 'p_mask': batch[5]}) outputs = model(**inputs) for i, example_index in enumerate(example_indices): eval_feature = features[example_index.item()] unique_id = int(eval_feature.unique_id) output = [to_list(output[i]) for output in outputs] # Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler" # models only use two. if len(output) >= 5: start_logits = output[0] start_top_index = output[1] end_logits = output[2] end_top_index = output[3] cls_logits = output[4] result = SquadResult( unique_id, start_logits, end_logits, start_top_index=start_top_index, end_top_index=end_top_index, cls_logits=cls_logits ) else: start_logits, end_logits = output result = SquadResult( unique_id, start_logits, end_logits ) all_results.append(result) evalTime = timeit.default_timer() - start_time logger.info(" Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset)) # Compute predictions output_prediction_file = os.path.join( args.output_dir, "predictions_{}.json".format(prefix)) output_nbest_file = os.path.join( args.output_dir, "nbest_predictions_{}.json".format(prefix)) if args.version_2_with_negative: output_null_log_odds_file = os.path.join( args.output_dir, "null_odds_{}.json".format(prefix)) else: output_null_log_odds_file = None # XLNet and XLM use a more complex post-processing procedure if args.model_type in ['xlnet', 'xlm']: start_n_top = model.config.start_n_top if hasattr( model, "config") else model.module.config.start_n_top end_n_top = model.config.end_n_top if hasattr( model, "config") else model.module.config.end_n_top predictions = compute_predictions_log_probs(examples, features, all_results, args.n_best_size, args.max_answer_length, output_prediction_file, output_nbest_file, output_null_log_odds_file, start_n_top, end_n_top, args.version_2_with_negative, tokenizer, args.verbose_logging) else: predictions = compute_predictions_logits(examples, features, all_results, args.n_best_size, args.max_answer_length, args.do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, args.verbose_logging, args.version_2_with_negative, args.null_score_diff_threshold) # Compute the F1 and exact scores. results = squad_evaluate(examples, predictions) return results def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False): if args.local_rank not in [-1, 0] and not evaluate: # Make sure only the first process in distributed training process the dataset, and the others will use the cache torch.distributed.barrier() # Load data features from cache or dataset file input_dir = args.data_dir if args.data_dir else "." cached_features_file = os.path.join(input_dir, 'cached_{}_{}_{}'.format( 'dev' if evaluate else 'train', list(filter(None, args.model_name_or_path.split('/'))).pop(), str(args.max_seq_length)) ) # Init features and dataset from cache if it exists if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples: logger.info("Loading features from cached file %s", cached_features_file) features_and_dataset = torch.load(cached_features_file) features, dataset = features_and_dataset["features"], features_and_dataset["dataset"] else: logger.info("Creating features from dataset file at %s", input_dir) if not args.data_dir: try: import tensorflow_datasets as tfds except ImportError: raise ImportError( "If not data_dir is specified, tensorflow_datasets needs to be installed.") if args.version_2_with_negative: logger.warn( "tensorflow_datasets does not handle version 2 of SQuAD.") tfds_examples = tfds.load("squad") examples = SquadV1Processor().get_examples_from_dataset( tfds_examples, evaluate=evaluate) else: processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor() examples = processor.get_dev_examples( args.data_dir) if evaluate else processor.get_train_examples(args.data_dir) features, dataset = squad_convert_examples_to_features( examples=examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=not evaluate, return_dataset='pt' ) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save({"features": features, "dataset": dataset}, cached_features_file) if args.local_rank == 0 and not evaluate: # Make sure only the first process in distributed training process the dataset, and the others will use the cache torch.distributed.barrier() if output_examples: return dataset, examples, features return dataset def main(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument("--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys())) parser.add_argument("--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS)) parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model checkpoints and predictions will be written.") # Other parameters parser.add_argument("--data_dir", default=None, type=str, help="The input data dir. Should contain the .json files for the task. If not specified, will run with tensorflow_datasets.") parser.add_argument("--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name") parser.add_argument("--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name") parser.add_argument("--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from s3") parser.add_argument('--version_2_with_negative', action='store_true', help='If true, the SQuAD examples contain some that do not have an answer.') parser.add_argument('--null_score_diff_threshold', type=float, default=0.0, help="If null_score - best_non_null is greater than the threshold predict null.") parser.add_argument("--max_seq_length", default=384, type=int, help="The maximum total input sequence length after WordPiece tokenization. Sequences " "longer than this will be truncated, and sequences shorter than this will be padded.") parser.add_argument("--doc_stride", default=128, type=int, help="When splitting up a long document into chunks, how much stride to take between chunks.") parser.add_argument("--max_query_length", default=64, type=int, help="The maximum number of tokens for the question. Questions longer than this will " "be truncated to this length.") parser.add_argument("--do_train", action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--evaluate_during_training", action='store_true', help="Rul evaluation during training at each logging step.") parser.add_argument("--do_lower_case", action='store_true', help="Set this flag if you are using an uncased model.") parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.") parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument('--gradient_accumulation_steps', type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.") parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument("--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.") parser.add_argument("--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.") parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument("--n_best_size", default=20, type=int, help="The total number of n-best predictions to generate in the nbest_predictions.json output file.") parser.add_argument("--max_answer_length", default=30, type=int, 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.") parser.add_argument("--verbose_logging", action='store_true', help="If true, all of the warnings related to data processing will be printed. " "A number of warnings are expected for a normal SQuAD evaluation.") parser.add_argument('--logging_steps', type=int, default=50, help="Log every X updates steps.") parser.add_argument('--save_steps', type=int, default=50, help="Save checkpoint every X updates steps.") parser.add_argument("--eval_all_checkpoints", action='store_true', help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number") parser.add_argument("--no_cuda", action='store_true', help="Whether not to use CUDA when available") parser.add_argument('--overwrite_output_dir', action='store_true', help="Overwrite the content of the output directory") parser.add_argument('--overwrite_cache', action='store_true', help="Overwrite the cached training and evaluation sets") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument('--fp16', action='store_true', help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit") parser.add_argument('--fp16_opt_level', type=str, default='O1', help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." "See details at https://nvidia.github.io/apex/amp.html") parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.") parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.") args = parser.parse_args() args.predict_file = os.path.join(args.output_dir, 'predictions_{}_{}.txt'.format( list(filter(None, args.model_name_or_path.split('/'))).pop(), str(args.max_seq_length)) ) if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir: raise ValueError( "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir)) # Setup distant debugging if needed if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach( address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() # Setup CUDA, GPU & distributed training if args.local_rank == -1 or args.no_cuda: device = torch.device( "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = torch.cuda.device_count() else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend='nccl') args.n_gpu = 1 args.device = device # Setup logging logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN) logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16) # Set seed set_seed(args) # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: # Make sure only the first process in distributed training will download model & vocab torch.distributed.barrier() args.model_type = args.model_type.lower() config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path, cache_dir=args.cache_dir if args.cache_dir else None) tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case, cache_dir=args.cache_dir if args.cache_dir else None) model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool( '.ckpt' in args.model_name_or_path), config=config, cache_dir=args.cache_dir if args.cache_dir else None) if args.local_rank == 0: # Make sure only the first process in distributed training will download model & vocab torch.distributed.barrier() model.to(args.device) logger.info("Training/evaluation parameters %s", args) # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set. # Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will # remove the need for this code, but it is still valid. if args.fp16: try: import apex apex.amp.register_half_function(torch, 'einsum') except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") # Training if args.do_train: train_dataset = load_and_cache_examples( args, tokenizer, evaluate=False, output_examples=False) global_step, tr_loss = train(args, train_dataset, model, tokenizer) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Save the trained model and the tokenizer if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): # Create output directory if needed if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) logger.info("Saving model checkpoint to %s", args.output_dir) # Save a trained model, configuration and tokenizer using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` # Take care of distributed/parallel training model_to_save = model.module if hasattr(model, 'module') else model model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) # Good practice: save your training arguments together with the trained model torch.save(args, os.path.join(args.output_dir, 'training_args.bin')) # Load a trained model and vocabulary that you have fine-tuned model = model_class.from_pretrained( args.output_dir, force_download=True) tokenizer = tokenizer_class.from_pretrained( args.output_dir, do_lower_case=args.do_lower_case) model.to(args.device) # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory results = {} if args.do_eval and args.local_rank in [-1, 0]: checkpoints = [args.output_dir] if args.eval_all_checkpoints: checkpoints = list(os.path.dirname(c) for c in sorted( glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True))) logging.getLogger("transformers.modeling_utils").setLevel( logging.WARN) # Reduce model loading logs logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: # Reload the model global_step = checkpoint.split( '-')[-1] if len(checkpoints) > 1 else "" model = model_class.from_pretrained( checkpoint, force_download=True) model.to(args.device) # Evaluate result = evaluate(args, model, tokenizer, prefix=global_step) result = dict((k + ('_{}'.format(global_step) if global_step else ''), v) for k, v in result.items()) results.update(result) logger.info("Results: {}".format(results)) return results if __name__ == "__main__": main()