clean up glue example
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@ -309,14 +309,7 @@ def main():
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# define a new function to compute loss values for both output_modes
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ouputs = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
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loss =
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if output_mode == "classification":
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
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elif output_mode == "regression":
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loss_fct = MSELoss()
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loss = loss_fct(logits.view(-1), label_ids.view(-1))
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loss = ouputs[0]
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if n_gpu > 1:
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loss = loss.mean() # mean() to average on multi-gpu.
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@ -423,15 +416,8 @@ def main():
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label_ids = label_ids.to(device)
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with torch.no_grad():
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logits = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask)
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# create eval loss and other metric required by the task
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if output_mode == "classification":
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loss_fct = CrossEntropyLoss()
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tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
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elif output_mode == "regression":
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loss_fct = MSELoss()
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tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))
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outputs = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
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tmp_eval_loss, logits = outputs[:2]
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eval_loss += tmp_eval_loss.mean().item()
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nb_eval_steps += 1
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@ -0,0 +1,401 @@
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""BERT finetuning runner."""
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from __future__ import absolute_import, division, print_function
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import argparse
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import logging
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import os
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import sys
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import random
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from tqdm import tqdm, trange
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import numpy as np
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import torch
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from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
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TensorDataset)
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from torch.utils.data.distributed import DistributedSampler
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from torch.nn import CrossEntropyLoss, MSELoss
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from tensorboardX import SummaryWriter
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from pytorch_transformers import WEIGHTS_NAME, CONFIG_NAME
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from pytorch_transformers.modeling_bert import BertForSequenceClassification
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from pytorch_transformers.tokenization_bert import BertTokenizer
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from pytorch_transformers.optimization import BertAdam, WarmupLinearSchedule
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from utils_glue import processors, output_modes, convert_examples_to_features, compute_metrics
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logger = logging.getLogger(__name__)
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def main():
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parser = argparse.ArgumentParser()
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## Required parameters
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parser.add_argument("--data_dir", default=None, type=str, required=True,
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help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
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parser.add_argument("--bert_model", default=None, type=str, required=True,
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help="Bert pre-trained model selected in the list: bert-base-uncased, "
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"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
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"bert-base-multilingual-cased, bert-base-chinese.")
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parser.add_argument("--task_name", default=None, type=str, required=True,
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help="The name of the task to train.")
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parser.add_argument("--output_dir", default=None, type=str, required=True,
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help="The output directory where the model predictions and checkpoints will be written.")
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## Other parameters
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parser.add_argument("--cache_dir", default="", type=str,
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help="Where do you want to store the pre-trained models downloaded from s3")
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parser.add_argument("--max_seq_length", default=128, type=int,
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help="The maximum total input sequence length after WordPiece tokenization. \n"
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"Sequences longer than this will be truncated, and sequences shorter \n"
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"than this will be padded.")
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parser.add_argument("--do_train", action='store_true',
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help="Whether to run training.")
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parser.add_argument("--do_eval", action='store_true',
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help="Whether to run eval on the dev set.")
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parser.add_argument("--do_lower_case", action='store_true',
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help="Set this flag if you are using an uncased model.")
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parser.add_argument("--train_batch_size", default=32, type=int,
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help="Total batch size for training.")
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parser.add_argument("--eval_batch_size", default=8, type=int,
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help="Total batch size for eval.")
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parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.")
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parser.add_argument("--learning_rate", default=5e-5, type=float,
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help="The initial learning rate for Adam.")
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parser.add_argument("--num_train_epochs", default=3.0, type=float,
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help="Total number of training epochs to perform.")
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parser.add_argument("--warmup_proportion", default=0.1, type=float,
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help="Proportion of training to perform linear learning rate warmup for. "
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"E.g., 0.1 = 10%% of training.")
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parser.add_argument("--no_cuda", action='store_true',
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help="Avoid using CUDA when available")
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parser.add_argument('--overwrite_output_dir', action='store_true',
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help="Overwrite the content of the output directory")
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parser.add_argument('--seed', type=int, default=42,
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help="random seed for initialization")
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parser.add_argument('--fp16', action='store_true',
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help="Whether to use 16-bit float precision instead of 32-bit")
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parser.add_argument('--loss_scale', type=float, default=0,
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help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
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"0 (default value): dynamic loss scaling.\n"
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"Positive power of 2: static loss scaling value.\n")
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parser.add_argument("--local_rank", type=int, default=-1,
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help="local_rank for distributed training on gpus")
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parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
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parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
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args = parser.parse_args()
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# Setup distant debugging if needed
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if args.server_ip and args.server_port:
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# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
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import ptvsd
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print("Waiting for debugger attach")
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ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
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ptvsd.wait_for_attach()
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# Setup CUDA, GPU & distributed training
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if args.local_rank == -1 or args.no_cuda:
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device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
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n_gpu = torch.cuda.device_count()
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else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
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torch.cuda.set_device(args.local_rank)
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device = torch.device("cuda", args.local_rank)
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torch.distributed.init_process_group(backend='nccl')
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n_gpu = 1
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args.device = device
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# Setup logging
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logging.basicConfig(level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
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logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
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device, n_gpu, bool(args.local_rank != -1), args.fp16))
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# Setup seeds
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random.seed(args.seed)
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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if n_gpu > 0:
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torch.cuda.manual_seed_all(args.seed)
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# Safety checks and create output directory
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if not args.do_train and not args.do_eval:
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raise ValueError("At least one of `do_train` or `do_eval` must be True.")
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if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
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raise ValueError("Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir))
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if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
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os.makedirs(args.output_dir)
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# Prepare GLUE task
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task_name = args.task_name.lower()
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if task_name not in processors:
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raise ValueError("Task not found: %s" % (task_name))
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processor = processors[task_name]()
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output_mode = output_modes[task_name]
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label_list = processor.get_labels()
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num_labels = len(label_list)
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# Load pretrained model and tokenizer
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if args.local_rank not in [-1, 0]:
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# Make sure only the first process in distributed training will download model & vocab
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torch.distributed.barrier()
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tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
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model = BertForSequenceClassification.from_pretrained(args.bert_model, num_labels=num_labels)
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if args.local_rank == 0:
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torch.distributed.barrier()
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# Distributed, parrallel and fp16 model
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if args.fp16:
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model.half()
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model.to(device)
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if args.local_rank != -1:
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model = torch.nn.parallel.DistributedDataParallel(model,
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device_ids=[args.local_rank],
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output_device=args.local_rank,
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find_unused_parameters=True)
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elif n_gpu > 1:
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model = torch.nn.DataParallel(model)
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global_step = 0
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tr_loss = 0
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if args.do_train:
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if args.local_rank in [-1, 0]:
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tb_writer = SummaryWriter()
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# Load and cache data
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train_examples = processor.get_train_examples(args.data_dir)
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cached_train_features_file = os.path.join(args.data_dir, 'train_{0}_{1}_{2}'.format(
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list(filter(None, args.bert_model.split('/'))).pop(), str(args.max_seq_length), str(task_name)))
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if os.path.exists(cached_train_features_file):
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train_features = torch.load(cached_train_features_file)
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else:
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train_features = convert_examples_to_features(
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train_examples, label_list, args.max_seq_length, tokenizer, output_mode)
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if args.local_rank == -1 or torch.distributed.get_rank() == 0:
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logger.info(" Saving train features into cached file %s", cached_train_features_file)
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torch.save(train_features, cached_train_features_file)
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# Convert in tensors and build dataloader
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all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
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all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
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all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
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if output_mode == "classification":
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all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
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elif output_mode == "regression":
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all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.float)
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args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
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train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
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train_sampler = RandomSampler(train_data) if args.local_rank == -1 else DistributedSampler(train_data)
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train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
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num_train_optimization_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
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# Prepare optimizer
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param_optimizer = list(model.named_parameters())
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no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
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optimizer_grouped_parameters = [
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{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
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{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
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]
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if args.fp16:
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try:
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from apex.optimizers import FP16_Optimizer, FusedAdam
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except ImportError:
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raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
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optimizer = FusedAdam(optimizer_grouped_parameters,
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lr=args.learning_rate,
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bias_correction=False,
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max_grad_norm=1.0)
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if args.loss_scale == 0:
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optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
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else:
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optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
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warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion,
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t_total=num_train_optimization_steps)
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else:
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optimizer = BertAdam(optimizer_grouped_parameters,
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lr=args.learning_rate,
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warmup=args.warmup_proportion,
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t_total=num_train_optimization_steps)
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# Train!
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logger.info("***** Running training *****")
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logger.info(" Num examples = %d", len(train_examples))
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logger.info(" Batch size = %d", args.train_batch_size)
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logger.info(" Num steps = %d", num_train_optimization_steps)
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model.train()
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for _ in trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]):
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for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
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batch = tuple(t.to(device) for t in batch)
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input_ids, input_mask, segment_ids, label_ids = batch
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ouputs = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
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loss = ouputs[0]
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if n_gpu > 1:
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loss = loss.mean() # mean() to average on multi-gpu parallel training
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if args.gradient_accumulation_steps > 1:
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loss = loss / args.gradient_accumulation_steps
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if args.fp16:
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optimizer.backward(loss)
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else:
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loss.backward()
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tr_loss += loss.item()
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if (step + 1) % args.gradient_accumulation_steps == 0:
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if args.fp16:
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# modify learning rate with special warm up BERT uses
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# if args.fp16 is False, BertAdam is used that handles this automatically
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lr_this_step = args.learning_rate * warmup_linear.get_lr(global_step, args.warmup_proportion)
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr_this_step
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optimizer.step()
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optimizer.zero_grad()
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global_step += 1
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if args.local_rank in [-1, 0]:
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if not args.fp16:
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tb_writer.add_scalar('lr', optimizer.get_lr()[0], global_step)
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tb_writer.add_scalar('loss', loss.item(), global_step)
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### Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
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### Example:
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if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
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# Save a trained model, configuration and tokenizer
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model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
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# If we save using the predefined names, we can load using `from_pretrained`
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output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
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output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
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torch.save(model_to_save.state_dict(), output_model_file)
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model_to_save.config.to_json_file(output_config_file)
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tokenizer.save_vocabulary(args.output_dir)
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# Load a trained model and vocabulary that you have fine-tuned
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model = BertForSequenceClassification.from_pretrained(args.output_dir)
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tokenizer = BertTokenizer.from_pretrained(args.output_dir)
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# Good practice: save your training arguments together with the trained model
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output_args_file = os.path.join(args.output_dir, 'training_args.bin')
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torch.save(args, output_args_file)
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else:
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model = BertForSequenceClassification.from_pretrained(args.bert_model)
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model.to(device)
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### Evaluation
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if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
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eval_task_names = ("mnli", "mnli-mm") if task_name == "mnli" else (task_name,)
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eval_outputs_dirs = (args.output_dir, args.output_dir + '-MM') if task_name == "mnli" else (args.output_dir,)
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for eval_task, output_dir in zip(eval_task_names, eval_outputs_dirs):
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if os.path.exists(output_dir) and os.listdir(output_dir) and args.do_train:
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raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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# Load and cache data
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processor = processors[eval_task]()
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eval_examples = processor.get_dev_examples(args.data_dir)
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cached_eval_features_file = os.path.join(args.data_dir, 'dev_{0}_{1}_{2}'.format(
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list(filter(None, args.bert_model.split('/'))).pop(), str(args.max_seq_length), str(eval_task)))
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if os.path.exists(cached_eval_features_file):
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eval_features = torch.load(cached_eval_features_file)
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else:
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eval_features = convert_examples_to_features(
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eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
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if args.local_rank == -1 or torch.distributed.get_rank() == 0:
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logger.info(" Saving eval features into cached file %s", cached_eval_features_file)
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torch.save(eval_features, cached_eval_features_file)
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# Convert in tensors and build dataloader
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all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
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all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
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all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
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if output_mode == "classification":
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all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
|
||||
elif output_mode == "regression":
|
||||
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.float)
|
||||
|
||||
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
|
||||
# Note that DistributedSampler samples randomly
|
||||
eval_sampler = SequentialSampler(eval_data) if args.local_rank == -1 else DistributedSampler(eval_data)
|
||||
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
|
||||
|
||||
# Eval!
|
||||
logger.info("***** Running evaluation *****")
|
||||
logger.info(" Num examples = %d", len(eval_examples))
|
||||
logger.info(" Batch size = %d", args.eval_batch_size)
|
||||
model.eval()
|
||||
eval_loss = 0
|
||||
nb_eval_steps = 0
|
||||
preds = None
|
||||
out_label_ids = None
|
||||
for batch in tqdm(eval_dataloader, desc="Evaluating"):
|
||||
batch = tuple(t.to(device) for t in batch)
|
||||
input_ids, input_mask, segment_ids, label_ids = batch
|
||||
|
||||
with torch.no_grad():
|
||||
outputs = model(input_ids,
|
||||
token_type_ids=segment_ids,
|
||||
attention_mask=input_mask,
|
||||
labels=label_ids)
|
||||
tmp_eval_loss, logits = outputs[:2]
|
||||
|
||||
eval_loss += tmp_eval_loss.mean().item()
|
||||
nb_eval_steps += 1
|
||||
if preds is None:
|
||||
preds = logits.detach().cpu().numpy()
|
||||
out_label_ids = label_ids.detach().cpu().numpy()
|
||||
else:
|
||||
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
|
||||
out_label_ids = np.append(out_label_ids, label_ids.detach().cpu().numpy(), axis=0)
|
||||
|
||||
eval_loss = eval_loss / nb_eval_steps
|
||||
if output_mode == "classification":
|
||||
preds = np.argmax(preds, axis=1)
|
||||
elif output_mode == "regression":
|
||||
preds = np.squeeze(preds)
|
||||
result = compute_metrics(eval_task, preds, out_label_ids)
|
||||
|
||||
loss = tr_loss/global_step if args.do_train else None
|
||||
|
||||
result['eval_loss'] = eval_loss
|
||||
result['global_step'] = global_step
|
||||
result['loss'] = loss
|
||||
|
||||
output_eval_file = os.path.join(output_dir, "eval_results.txt")
|
||||
with open(output_eval_file, "w") as writer:
|
||||
logger.info("***** Eval results *****")
|
||||
for key in sorted(result.keys()):
|
||||
logger.info(" %s = %s", key, str(result[key]))
|
||||
writer.write("%s = %s\n" % (key, str(result[key])))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
|
@ -583,6 +583,7 @@ processors = {
|
|||
output_modes = {
|
||||
"cola": "classification",
|
||||
"mnli": "classification",
|
||||
"mnli-mm": "classification",
|
||||
"mrpc": "classification",
|
||||
"sst-2": "classification",
|
||||
"sts-b": "regression",
|
||||
|
|
|
@ -110,6 +110,24 @@ class PreTrainedTokenizer(object):
|
|||
|
||||
return tokenizer
|
||||
|
||||
def tokenize(self, text):
|
||||
raise NotImplementedError
|
||||
|
||||
def convert_tokens_to_ids(self, tokens):
|
||||
raise NotImplementedError
|
||||
|
||||
def convert_ids_to_tokens(self, ids):
|
||||
raise NotImplementedError
|
||||
|
||||
def encode(self, text):
|
||||
raise NotImplementedError
|
||||
|
||||
def decode(self, token_ids, *input, **kwargs):
|
||||
raise NotImplementedError
|
||||
|
||||
def save_vocabulary(self, vocab_path):
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
def clean_up_tokenization(out_string):
|
||||
out_string.replace(' .', '.').replace(' ?', '?').replace(' !', '!').replace(' ,', ','
|
||||
|
|
Loading…
Reference in New Issue