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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -474,7 +474,6 @@ def evaluate(args, model, tokenizer, prefix, label_map): logger.info("\n%s", report) writer.write(report) def load_and_cache_examples(args, task, tokenizer, evaluate=False): @@ -710,8 +709,8 @@ def main(): model.to(args.device) label_map = {i : label for i, label in enumerate(label_list,1)} result = evaluate(args, model, tokenizer, prefix=global_step, label_map=label_map) #result = dict((k + '_{}'.format(global_step), v) for k, v in result.items()) #results.update(result) return results -
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Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,720 @@ from __future__ import absolute_import, division, print_function import argparse import glob import logging import os import random import numpy as np import torch from torch import nn import torch.nn.functional as F from torch.nn import CrossEntropyLoss from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset) from torch.utils.data.distributed import DistributedSampler from tensorboardX import SummaryWriter from tqdm import tqdm, trange from pytorch_transformers import (WEIGHTS_NAME, BertConfig, BertForTokenClassification, BertForSequenceClassification, BertTokenizer, RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer, XLMConfig, XLMForSequenceClassification, XLMTokenizer, XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer) from pytorch_transformers import AdamW, WarmupLinearSchedule from seqeval.metrics import classification_report from utils_glue import compute_metrics # Prepare GLUE task output_modes = { "ner": "classification", } class Ner(BertForTokenClassification): def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None,valid_ids=None,attention_mask_label=None): #sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask) batch_size,max_len,feat_dim = sequence_output.shape valid_output = torch.zeros(batch_size,max_len,feat_dim,dtype=torch.float32,device='cuda') for i in range(batch_size): jj = -1 for j in range(max_len): if valid_ids[i][j].item() == 1: jj += 1 valid_output[i][jj] = sequence_output[i][j] sequence_output = self.dropout(valid_output) logits = self.classifier(sequence_output) if labels is not None: loss_fct = CrossEntropyLoss(ignore_index=0) # Only keep active parts of the loss attention_mask_label = None if attention_mask_label is not None: active_loss = attention_mask_label.view(-1) == 1 active_logits = logits.view(-1, self.num_labels)[active_loss] active_labels = labels.view(-1)[active_loss] loss = loss_fct(active_logits, active_labels) else: loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) return loss else: return logits class InputExample(object): """A single training/test example for simple sequence classification.""" def __init__(self, guid, text_a, text_b=None, label=None): """Constructs a InputExample. Args: guid: Unique id for the example. text_a: string. The untokenized text of the first sequence. For single sequence tasks, only this sequence must be specified. text_b: (Optional) string. The untokenized text of the second sequence. Only must be specified for sequence pair tasks. label: (Optional) string. The label of the example. This should be specified for train and dev examples, but not for test examples. """ self.guid = guid self.text_a = text_a self.text_b = text_b self.label = label class InputFeatures(object): """A single set of features of data.""" def __init__(self, input_ids, input_mask, segment_ids, label_id, valid_ids=None, label_mask=None): self.input_ids = input_ids self.input_mask = input_mask self.segment_ids = segment_ids self.label_id = label_id self.valid_ids = valid_ids self.label_mask = label_mask def readfile(filename): ''' read file ''' f = open(filename) data = [] sentence = [] label= [] for line in f: if len(line)==0 or line.startswith('-DOCSTART') or line[0]=="\n": if len(sentence) > 0: data.append((sentence,label)) sentence = [] label = [] continue splits = line.split(' ') sentence.append(splits[0]) label.append(splits[-1][:-1]) if len(sentence) >0: data.append((sentence,label)) sentence = [] label = [] return data class DataProcessor(object): """Base class for data converters for sequence classification data sets.""" def get_train_examples(self, data_dir): """Gets a collection of `InputExample`s for the train set.""" raise NotImplementedError() def get_dev_examples(self, data_dir): """Gets a collection of `InputExample`s for the dev set.""" raise NotImplementedError() def get_labels(self): """Gets the list of labels for this data set.""" raise NotImplementedError() @classmethod def _read_tsv(cls, input_file, quotechar=None): """Reads a tab separated value file.""" return readfile(input_file) class NerProcessor(DataProcessor): """Processor for the CoNLL-2003 data set.""" def get_train_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.txt")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "valid.txt")), "dev") def get_test_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "test.txt")), "test") def get_labels(self): return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "[CLS]", "[SEP]"] def _create_examples(self,lines,set_type): examples = [] for i,(sentence,label) in enumerate(lines): guid = "%s-%s" % (set_type, i) text_a = ' '.join(sentence) text_b = None label = label examples.append(InputExample(guid=guid,text_a=text_a,text_b=text_b,label=label)) return examples def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer): """Loads a data file into a list of `InputBatch`s.""" label_map = {label : i for i, label in enumerate(label_list,1)} features = [] for (ex_index,example) in enumerate(examples): textlist = example.text_a.split(' ') labellist = example.label tokens = [] labels = [] valid = [] label_mask = [] for i, word in enumerate(textlist): token = tokenizer.tokenize(word) tokens.extend(token) label_1 = labellist[i] for m in range(len(token)): if m == 0: labels.append(label_1) valid.append(1) label_mask.append(1) else: valid.append(0) if len(tokens) >= max_seq_length - 1: tokens = tokens[0:(max_seq_length - 2)] labels = labels[0:(max_seq_length - 2)] valid = valid[0:(max_seq_length - 2)] label_mask = label_mask[0:(max_seq_length - 2)] ntokens = [] segment_ids = [] label_ids = [] ntokens.append("[CLS]") segment_ids.append(0) valid.insert(0,1) label_mask.insert(0,1) label_ids.append(label_map["[CLS]"]) for i, token in enumerate(tokens): ntokens.append(token) segment_ids.append(0) if len(labels) > i: label_ids.append(label_map[labels[i]]) ntokens.append("[SEP]") segment_ids.append(0) valid.append(1) label_mask.append(1) label_ids.append(label_map["[SEP]"]) input_ids = tokenizer.convert_tokens_to_ids(ntokens) input_mask = [1] * len(input_ids) label_mask = [1] * len(label_ids) while len(input_ids) < max_seq_length: input_ids.append(0) input_mask.append(0) segment_ids.append(0) label_ids.append(0) valid.append(1) label_mask.append(0) while len(label_ids) < max_seq_length: label_ids.append(0) label_mask.append(0) assert len(input_ids) == max_seq_length assert len(input_mask) == max_seq_length assert len(segment_ids) == max_seq_length assert len(label_ids) == max_seq_length assert len(valid) == max_seq_length assert len(label_mask) == max_seq_length if ex_index < 5: logger.info("*** Example ***") logger.info("guid: %s" % (example.guid)) logger.info("tokens: %s" % " ".join( [str(x) for x in tokens])) logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) logger.info( "segment_ids: %s" % " ".join([str(x) for x in segment_ids])) # logger.info("label: %s (id = %d)" % (example.label, label_ids)) features.append( InputFeatures(input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_id=label_ids, valid_ids=valid, label_mask=label_mask)) return features logger = logging.getLogger(__name__) ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLNetConfig, XLMConfig, RobertaConfig)), ()) MODEL_CLASSES = { 'bert': (BertConfig, BertForSequenceClassification, BertTokenizer), 'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer), 'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer), 'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer), } 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 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 = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total) 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 = 0 tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]) set_seed(args) # Added here for reproductibility (even between python 2 and 3) 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): model.train() batch = tuple(t.to(args.device) for t in batch) input_ids, input_mask, segment_ids, label_ids, valid_ids,l_mask = batch inputs = {'input_ids': batch[0], 'attention_mask': batch[1], 'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None, # XLM and RoBERTa don't use segment_ids 'labels': batch[3]} #outputs = model(**inputs) outputs = model(input_ids, segment_ids, input_mask, label_ids,valid_ids,l_mask) loss = outputs #[0] # model outputs are always tuple in pytorch-transformers (see doc) if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel 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() torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0: scheduler.step() # Update learning rate schedule optimizer.step() model.zero_grad() global_step += 1 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: # Log metrics if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well 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 if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # Save model checkpoint output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step)) if not os.path.exists(output_dir): os.makedirs(output_dir) model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training model_to_save.save_pretrained(output_dir) torch.save(args, os.path.join(output_dir, 'training_args.bin')) logger.info("Saving model checkpoint 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, label_map): # Loop to handle MNLI double evaluation (matched, mis-matched) eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,) eval_outputs_dirs = (args.output_dir, args.output_dir + '-MM') if args.task_name == "mnli" else (args.output_dir,) results = {} for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs): eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True) if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]: os.makedirs(eval_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(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # Eval! logger.info("***** Running evaluation {} *****".format(prefix)) logger.info(" Num examples = %d", len(eval_dataset)) logger.info(" Batch size = %d", args.eval_batch_size) eval_loss = 0.0 nb_eval_steps = 0 preds = None out_label_ids = None y_true = [] y_pred = [] for batch in tqdm(eval_dataloader, desc="Evaluating"): model.eval() batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): input_ids, input_mask, segment_ids, label_ids, valid_ids,l_mask = batch outputs = model(input_ids, segment_ids, input_mask,valid_ids=valid_ids,attention_mask_label=l_mask) logits = outputs #[:2] logits = torch.argmax(F.log_softmax(logits,dim=2),dim=2) logits = logits.detach().cpu().numpy() label_ids = label_ids.to('cpu').numpy() input_mask = input_mask.to('cpu').numpy() for i, label in enumerate(label_ids): temp_1 = [] temp_2 = [] for j,m in enumerate(label): if j == 0: continue elif label_ids[i][j] == len(label_map): y_true.append(temp_1) y_pred.append(temp_2) break else: temp_1.append(label_map[label_ids[i][j]]) temp_2.append(label_map[logits[i][j]]) report = classification_report(y_true, y_pred,digits=4) logger.info("\n%s", report) output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") logger.info("\n%s", report) writer.write(report) return results def load_and_cache_examples(args, task, tokenizer, evaluate=False): if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache processor = NerProcessor() output_mode = output_modes[task] # Load data features from cache or dataset file cached_features_file = os.path.join(args.data_dir, 'cached_{}_{}_{}_{}'.format( 'dev' if evaluate else 'train', list(filter(None, args.model_name_or_path.split('/'))).pop(), str(args.max_seq_length), str(task))) if os.path.exists(cached_features_file): logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) else: logger.info("Creating features from dataset file at %s", args.data_dir) label_list = processor.get_labels() if task in ['mnli', 'mnli-mm'] and args.model_type in ['roberta']: # HACK(label indices are swapped in RoBERTa pretrained model) label_list[1], label_list[2] = label_list[2], label_list[1] examples = processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir) # Changed features = convert_examples_to_features(examples, label_list, args.max_seq_length, tokenizer) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file) if args.local_rank == 0 and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long) all_valid_ids = torch.tensor([f.valid_ids for f in features], dtype=torch.long) all_lmask_ids = torch.tensor([f.label_mask for f in features], dtype=torch.long) dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids,all_valid_ids,all_lmask_ids) #dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) return dataset def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument("--data_dir", default=None, type=str, required=True, help="The input data dir. Should contain the .tsv files (or other data files) for the task.") 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("--task_name", default="ner", type=str, required=True, help="The name of the task to train selected in the list: ner") parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.") ## Other parameters 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("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded.") 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('--gradient_accumulation_steps', type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.") parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight deay 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('--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="Avoid using 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('--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("--local_rank", type=int, default=-1, help="For distributed training: local_rank") parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.") parser.add_argument('--server_port', type=str, default='', help="For distant debugging.") args = parser.parse_args() 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) processors = {'ner': NerProcessor} processor = processors[args.task_name]() args.output_mode = output_modes[args.task_name] label_list = processor.get_labels() num_labels = len(label_list) + 1 # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab 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, num_labels=num_labels, finetuning_task=args.task_name) 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) model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config) model = Ner.from_pretrained(args.model_name_or_path, num_labels=num_labels) if args.local_rank == 0: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab model.to(args.device) logger.info("Training/evaluation parameters %s", args) # Training if args.do_train: train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False) global_step, tr_loss = train(args, train_dataset, model, tokenizer) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained() 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()` model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training 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) tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) model.to(args.device) # Evaluation results = {} if args.do_eval and args.local_rank in [-1, 0]: tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) 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("pytorch_transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else "" model = model_class.from_pretrained(checkpoint) model = Ner.from_pretrained(checkpoint, num_labels=num_labels) model.to(args.device) label_map = {i : label for i, label in enumerate(label_list,1)} result = evaluate(args, model, tokenizer, prefix=global_step, label_map=label_map) result = dict((k + '_{}'.format(global_step), v) for k, v in result.items()) results.update(result) return results if __name__ == "__main__": main()