def __init__(self):
''' PRE-LOAD NECESSARY DATA '''
# print(os.path.join('models', 'sbert.net_models_distilbert-base-nli-stsb-mean-tokens'))
# print(os.path.join('models', 'albert_t'))
# print(os.path.join('models', 'albert_m'))
# print(os.getcwd())
self.__sentence_model = SentenceTransformer(
os.path.join(
'models',
'sbert.net_models_distilbert-base-nli-stsb-mean-tokens'))
self.__tokenizer = AlbertTokenizer.from_pretrained(
os.path.join('models', 'albert_t'))
self.__model = AlbertForQuestionAnswering.from_pretrained(
os.path.join('models', 'albert_m'))
# Read url file
with open(os.path.join('data', 'urls.txt'), 'r') as file:
self.urls = file.read().splitlines()
file.close()
with open(os.path.join('data', 'titles.txt'), 'r') as file:
self.titles = file.read().splitlines()
file.close()
# Load pickle files into variables
names = [
os.path.join('data', 'punctuated.pkl'),
os.path.join('data', 'punctuated_embed.pkl'),
os.path.join('data', 'subs.pkl')
]
self.__punctuateds, self.__sentence_embeddings_p, self.__subs = tuple(
map(loadPickle, names))
''' END OF PRE-LOAD NECESSARY DATA '''
def build(args):
TAG = create_tags()
XLSX_PATH = {'train': 'release/train/ca_data', 'dev': 'release/dev/ca_data', 'test': 'release/test/ca_data'}
PRETRAINED_MODEL_NAME = 'ALINEAR/albert-japanese-v2'
tokenizer = AlbertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)
train_data = TrainData(XLSX_PATH['train'], TAG, only_positive=args.only_positive)
trainset = QADataset(train_data.examples, "train", tokenizer=tokenizer)
trainloader = DataLoader(trainset, batch_size=args.batch_size, collate_fn=collate_fn)
dev_data = TrainData(XLSX_PATH['dev'], TAG, only_positive=args.only_positive)
devset = QADataset(dev_data.examples, "train", tokenizer=tokenizer)
devloader = DataLoader(devset, batch_size=args.batch_size, collate_fn=collate_fn)
logger.info(f"[train data] {train_data.summary()}")
logger.info(f"[dev data] {dev_data.summary()}")
test_data = TestData(XLSX_PATH['dev'], TAG)
testset = QADataset(test_data.examples, "test", tokenizer=tokenizer)
testloader = DataLoader(testset, batch_size=args.batch_size, collate_fn=collate_fn)
model = AlbertForQuestionAnswering.from_pretrained(PRETRAINED_MODEL_NAME)
model = model.to(args.device)
if args.load_pretrained_model:
model.load_state_dict(torch.load(args.pretrained_model_path))
return model, trainloader, devloader, testloader, tokenizer
def __init__(self, name, path: str, gpu=False):
self.tokenizer = AlbertTokenizer.from_pretrained(path)
pretrained_albert_model = AlbertForQuestionAnswering.from_pretrained(
path)
super().__init__(name, pretrained_albert_model, gpu)
if self.gpu:
self.predictor.cuda()
def load_and_predict(data_dir, model_type, pretrain_model):
if model_type == 'bert_japanese':
model = BertForQuestionAnswering.from_pretrained(
'cl-tohoku/bert-base-japanese')
tokenizer = BertJapaneseTokenizer.from_pretrained(
'cl-tohoku/bert-base-japanese')
if model_type == 'bert_multilingual':
model = BertForQuestionAnswering.from_pretrained(
'bert-base-multilingual-cased')
tokenizer = BertTokenizer.from_pretrained(
'bert-base-multilingual-cased', tokenize_chinese_chars=False)
if model_type == 'albert':
model = AlbertForQuestionAnswering.from_pretrained(
'ALINEAR/albert-japanese-v2')
tokenizer = AlbertTokenizer.from_pretrained(
'ALINEAR/albert-japanese-v2')
test_data = TestData(data_dir, TAG)
testset = QADataset(test_data.examples, "test", tokenizer=tokenizer)
testloader = DataLoader(testset, batch_size=4, collate_fn=collate_fn)
model = model.to(device)
model.load_state_dict(torch.load(pretrain_model))
prediction = predict(model, testloader, device, tokenizer)
prediction = func(data_dir, prediction)
print('finish loading and predicting from {}!'.format(pretrain_model))
return prediction #prediction dictionary
def load_model(self, model_path: str, do_lower_case=True):
config = AlbertConfig.from_pretrained(model_path + "/config.json")
tokenizer = AlbertTokenizer.from_pretrained(model_path)
#tokenizer = AlbertTokenizer.from_pretrained('albert-large-v2', do_lower_case=do_lower_case)
model = AlbertForQuestionAnswering.from_pretrained(model_path,
from_tf=False,
config=config)
return model, tokenizer
def load_model(pretrained_model):
# Other models to try: albert-large-v2, albert-xlarge-v2
# https://huggingface.co/transformers/pretrained_models.html
tokenizer = AlbertTokenizer.from_pretrained(pretrained_model,
do_lower_case=True)
model = AlbertForQuestionAnswering.from_pretrained(pretrained_model,
cache_dir="/usr/cache")
return model, tokenizer
def load_models():
# Download files locally if not exist from S3 bucket
s3_bucket = boto3.resource('s3').Bucket('albert-model-files')
for object in s3_bucket.objects.all():
if object.key in ["config.json", "vocab.txt", "pytorch_model.bin"]:
if not os.path.exists('model_data/{}'.format(object.key)):
s3_bucket.download_file(object.key,
'model_data/{}'.format(object.key))
for object in s3_bucket.objects.all():
if object.key in [
"special_tokens_map.json", "spiece.model",
"tokenizer_config.json"
]:
if not os.path.exists('tokenizer_albert/{}'.format(object.key)):
s3_bucket.download_file(
object.key, 'tokenizer_albert/{}'.format(object.key))
# Load pretrained models
tokenizer = AlbertTokenizer.from_pretrained('./tokenizer_albert')
model = AlbertForQuestionAnswering.from_pretrained('./model_data')
return model, tokenizer
def main(args):
if args.large:
args.train_record_file += '_large'
args.dev_eval_file += '_large'
model_name = "albert-xlarge-v2"
else:
model_name = "albert-base-v2"
if args.xxlarge:
args.train_record_file += '_xxlarge'
args.dev_eval_file += '_xxlarge'
model_name = "albert-xxlarge-v2"
# Set up logging and devices
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=True)
log = util.get_logger(args.save_dir, args.name)
tbx = SummaryWriter(args.save_dir)
device, args.gpu_ids = util.get_available_devices()
log.info(f'Args: {dumps(vars(args), indent=4, sort_keys=True)}')
args.batch_size *= max(1, len(args.gpu_ids))
# Set random seed
log.info(f'Using random seed {args.seed}...')
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# Get model
log.info('Building model...')
if args.bidaf:
char_vectors = util.torch_from_json(args.char_emb_file)
if args.model_name == 'albert_highway':
model = models.albert_highway(model_name)
elif args.model_name == 'albert_lstm_highway':
model = models.LSTM_highway(model_name, hidden_size=args.hidden_size)
elif args.model_name == 'albert_bidaf':
model = models.BiDAF(char_vectors=char_vectors,
hidden_size=args.hidden_size,
drop_prob=args.drop_prob)
elif args.model_name == 'albert_bidaf2':
model = models.BiDAF2(model_name=model_name,
char_vectors=char_vectors,
hidden_size=args.hidden_size,
drop_prob=args.drop_prob)
else:
model = AlbertForQuestionAnswering.from_pretrained(args.model_name)
model = nn.DataParallel(model, args.gpu_ids)
if args.load_path:
log.info(f'Loading checkpoint from {args.load_path}...')
model, step = util.load_model(model, args.load_path, args.gpu_ids)
else:
step = 0
model = model.to(device)
model.train()
ema = util.EMA(model, args.ema_decay)
# Get saver
saver = util.CheckpointSaver(args.save_dir,
max_checkpoints=args.max_checkpoints,
metric_name=args.metric_name,
maximize_metric=args.maximize_metric,
log=log)
# Get optimizer and scheduler
optimizer = AdamW(model.parameters(), lr=args.lr, weight_decay=args.l2_wd)
scheduler = sched.LambdaLR(optimizer, lambda s: 1.) # Constant LR
# Get data loader
log.info('Building dataset...')
train_dataset = SQuAD(args.train_record_file, args.use_squad_v2,
args.bidaf)
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers)
dev_dataset = SQuAD(args.dev_eval_file, args.use_squad_v2, args.bidaf)
dev_loader = data.DataLoader(dev_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers)
with open(args.dev_gold_file) as f:
gold_dict = json.load(f)
tokenizer = AlbertTokenizer.from_pretrained(model_name)
# Train
log.info('Training...')
steps_till_eval = args.eval_steps
epoch = step // len(train_dataset)
while epoch != args.num_epochs:
epoch += 1
log.info(f'Starting epoch {epoch}...')
with torch.enable_grad(), \
tqdm(total=len(train_loader.dataset)) as progress_bar:
for batch in train_loader:
batch = tuple(t.to(device) for t in batch)
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
'start_positions': batch[3],
'end_positions': batch[4],
}
if args.bidaf:
inputs['char_ids'] = batch[6]
y1 = batch[3]
y2 = batch[4]
# Setup for forward
batch_size = inputs["input_ids"].size(0)
optimizer.zero_grad()
# Forward
# log_p1, log_p2 = model(**inputs)
y1, y2 = y1.to(device), y2.to(device)
outputs = model(**inputs)
loss = outputs[0]
loss = loss.mean()
# loss_fct = nn.CrossEntropyLoss()
# loss = loss_fct(log_p1, y1) + loss_fct(log_p2, y2)
# loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
loss_val = loss.item()
# Backward
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step(step // batch_size)
ema(model, step // batch_size)
# Log info
step += batch_size
progress_bar.update(batch_size)
progress_bar.set_postfix(epoch=epoch, NLL=loss_val)
tbx.add_scalar('train/NLL', loss_val, step)
tbx.add_scalar('train/LR', optimizer.param_groups[0]['lr'],
step)
steps_till_eval -= batch_size
if steps_till_eval <= 0:
steps_till_eval = args.eval_steps
# Evaluate and save checkpoint
log.info(f'Evaluating at step {step}...')
ema.assign(model)
results, pred_dict = evaluate(args, model, dev_dataset,
dev_loader, gold_dict,
tokenizer, device,
args.max_ans_len,
args.use_squad_v2)
saver.save(step, model, results[args.metric_name], device)
ema.resume(model)
# Log to console
results_str = ', '.join(f'{k}: {v:05.2f}'
for k, v in results.items())
log.info(f'Dev {results_str}')
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar(f'dev/{k}', v, step)
else:
write_in_result_file("Bert")
write_in_result_file("k = " + str(k))
if use_ir_score:
write_in_result_file('Using IR score with mu = ' +
str(mu_bench))
else:
write_in_result_file('Not using IR score')
if use_albert:
if not use_dil:
tokenizer = AlbertTokenizer.from_pretrained(
args.albert_path, do_lower_case=True)
model = AlbertForQuestionAnswering.from_pretrained(
args.albert_path)
else:
tokenizer = AlbertTokenizer.from_pretrained(
args.dilalbert_path, do_lower_case=True)
model = DilAlbert.from_pretrained(
args.dilalbert_path)
else:
if not use_dil:
tokenizer = BertTokenizer.from_pretrained(
args.bert_path, do_lower_case=True)
model = BertForQuestionAnswering.from_pretrained(
args.bert_path)
else:
tokenizer = BertTokenizer.from_pretrained(
args.dilbert_path, do_lower_case=True)
model = DilBert.from_pretrained(args.dilbert_path)
# ner_reljson=tkitFile.Json("../tdata/onlyner/dev.json")
# i=0
# all=0
# # ner_list=ner_plus(text)
# for item in ner_reljson.auto_load():
# The checkpoint albert-base-v2 is not fine-tuned for question answering. Please see the
# examples/run_squad.py example to see how to fine-tune a model to a question answering task.
from transformers import AlbertTokenizer, AlbertForQuestionAnswering, BertTokenizer, AlbertConfig
import torch
# tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
tokenizer = BertTokenizer.from_pretrained('tkitfiles/qa/model/')
# config=AlbertConfig.from_pretrained('tkitfiles/qa/model/config.json')
model = AlbertForQuestionAnswering.from_pretrained('tkitfiles/qa/model/')
data = tkitFile.Json("../tdata/SQuAD/dev.json")
i = 0
all = 0
f = 0
for item in data.auto_load():
for one in item['data']:
all = all + 1
# print(one['paragraphs'][0])
# print(one['paragraphs'][0]['context'])
question, text = one['paragraphs'][0]['qas'][0]['question'], one[
'paragraphs'][0]['context']
# question, text = "利比里亚共和国", "利比里亚共和国（英语：'） 通称赖比瑞亚，是位于西非，北接几内亚，西北界塞拉利昂，东邻象牙海岸，西南濒大西洋的总统制共和国家"
input_dict = tokenizer.encode_plus(question, text, return_tensors='pt')
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--train_file",
default=None,
type=str,
required=True,
help="SQuAD json for training. E.g., train-v1.1.json")
parser.add_argument(
"--predict_file",
default=None,
type=str,
required=True,
help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json"
)
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(
"--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,
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=500,
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()
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]:
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,
# 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)
config = AlbertConfig.from_pretrained(args.model_name_or_path +
"/config.json")
tokenizer = AlbertTokenizer.from_pretrained(
'albert-large-v2', do_lower_case=args.do_lower_case)
model = AlbertForQuestionAnswering.from_pretrained(args.model_name_or_path,
from_tf=False,
config=config)
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)
# 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()`
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,
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)
"""
results = evaluate(args, model, tokenizer)
print(results)
logger.info("Results: {}".format(results))
return results
def do_prediction(model_dir, model_name, questions_dir):
# 1. Load a trained model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AlbertForQuestionAnswering.from_pretrained(model_dir)
model.to(device)
model.eval()
# 2. Load and pre-process the test set
dev_file = questions_dir #"data/sfu.json"
predict_batch_size = 2
max_seq_length = 384
eval_examples = read_squad_examples(input_file=dev_file,
is_training=False,
version_2_with_negative=False)
tokenizer = AlbertTokenizer.from_pretrained(model_dir)
eval_features = convert_examples_to_features(examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=128,
max_query_length=64,
is_training=False)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_example_index)
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=predict_batch_size)
# 3. Run inference on the test set
all_results = []
for input_ids, input_mask, segment_ids, example_indices in tqdm(
eval_dataloader):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
with torch.no_grad():
batch_start_logits, batch_end_logits = model(
input_ids, input_mask, segment_ids)
for i, example_index in enumerate(example_indices):
start_logits = batch_start_logits[i].detach().cpu().tolist()
end_logits = batch_end_logits[i].detach().cpu().tolist()
eval_feature = eval_features[example_index.item()]
unique_id = int(eval_feature.unique_id)
all_results.append(
RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
output_prediction_file = os.path.join(
model_dir, model_name + "_albert_predictions_sfu.json")
output_nbest_file = os.path.join(
model_dir, model_name + "_albert_nbest_predictions_sfu.json")
output_null_log_odds_file = os.path.join(
model_dir, model_name + "_null_odds_sfu.json")
preds = write_predictions(eval_examples, eval_features, all_results, 20,
30, True, output_prediction_file,
output_nbest_file, output_null_log_odds_file,
True, False, 0.0)
"input_ids": torch.cat((input_ids,inputs_par[i]["input_ids"]),1),
"attention_mask": torch.cat((attention_mask,inputs_par[i]["attention_mask"]),1),
"token_type_ids": torch.cat((token_type_ids,inputs_par[i]["token_type_ids"]),1),
}
start = time.time()
outputs = model.process_B(preprocessed_question, preprocessed_paragraph,**inputs)
total_time_questions_paragraphs_pairs = total_time_questions_paragraphs_pairs + time.time() - start
print("NI Q DilAlbert :", total_time_questions)
print("I Q-P DilAlbert :", total_time_questions_paragraphs_pairs)
total_dilalbert = total_time_passages+total_time_questions+total_time_questions_paragraphs_pairs
print("Total DilAlbert : ", total_dilalbert)
tokenizer = AlbertTokenizer.from_pretrained(ALBERT_PATH, do_lower_case=True)
model = AlbertForQuestionAnswering.from_pretrained(ALBERT_PATH)
model.to(torch.device(device))
total_time_questions_paragraphs_pairs_albert = 0
eval_dataloader = DataLoader(dataset, batch_size=1)
for question in squad1_for_orqa["questions"][:n_questions]:
input_ids = torch.tensor([tokenizer.encode(question)], device=device)
attention_mask = torch.tensor([[1]*input_ids.shape[1]], device=device)
token_type_ids = torch.tensor([[0]*input_ids.shape[1]], device=device)
for batch in eval_dataloader:
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": torch.cat((input_ids,batch[0][:,3:]),1),
"attention_mask": torch.cat((attention_mask,batch[1][:,3:]),1),
"token_type_ids": torch.cat((token_type_ids,batch[2][:,3:]),1),
class SquadDataset(torch.utils.data.Dataset):
def __init__(self, encodings):
self.encodings = encodings
def __getitem__(self, idx):
return {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
def __len__(self):
return len(self.encodings.input_ids)
train_dataset = SquadDataset(train_encodings)
val_dataset = SquadDataset(val_encodings)
from transformers import AlbertForQuestionAnswering
model = AlbertForQuestionAnswering.from_pretrained("albert-base-v2")
from torch.utils.data import DataLoader
from transformers import AdamW
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)
model.train()
train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True)
optim = AdamW(model.parameters(), lr=5e-5)
for epoch in range(3):
for batch in train_loader:
def document_retriever(question):
query_bow = dictionary.doc2bow(jieba.cut(question,cut_all=False))
tfidfvect = tfidf[query_bow]
simstfidf = indexTfidf[tfidfvect]
return [context[i] for i in (-simstfidf).argsort()[0:1]]#返回前3名
model_path = 'voidful/albert_chinese_base'
tokenizer_kwards = {'do_lower_case': False,'max_len': 512}
tokenizer = BertTokenizer.from_pretrained(model_path, **tokenizer_kwards)
from transformers import AlbertForQuestionAnswering,AutoConfig
model_path = 'voidful/albert_chinese_base'
bert_config = AutoConfig.from_pretrained(model_path)
model = AlbertForQuestionAnswering.from_pretrained(r"checkpoint_score_f1-86.233_em-66.853.pth",
**{'config':bert_config}).to('cuda')
import torch
from torch.utils.data import TensorDataset, DataLoader
SPIECE_UNDERLINE = '▁'
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answer."""
# The SQuAD annotations are character based. We first project them to
# whitespace-tokenized words. But then after WordPiece tokenization, we can
# often find a "better match". For example:
#
# Question: What year was John Smith born?
# Context: The leader was John Smith (1895-1943).
import torch
from transformers import AlbertTokenizer, AlbertForQuestionAnswering
tokenizer = AlbertTokenizer.from_pretrained(
'ahotrod/albert_xxlargev1_squad2_512')
model = AlbertForQuestionAnswering.from_pretrained(
'ahotrod/albert_xxlargev1_squad2_512')
def answer(question, text):
input_dict = tokenizer.encode_plus(question,
text,
return_tensors='pt',
max_length=512)
input_ids = input_dict["input_ids"].tolist()
start_scores, end_scores = model(**input_dict)
start = torch.argmax(start_scores)
end = torch.argmax(end_scores)
all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
answer = ''.join(all_tokens[start:end + 1]).replace('▁', ' ').strip()
answer = answer.replace('[SEP]', '')
return answer if answer != '[CLS]' and len(
answer) != 0 else 'could not find an answer'
import torch
from transformers import AlbertTokenizer, AlbertForQuestionAnswering
tokenizer = AlbertTokenizer.from_pretrained('twmkn9/albert-base-v2-squad2')
model = AlbertForQuestionAnswering.from_pretrained(
'twmkn9/albert-base-v2-squad2')
def answer(question, text):
input_dict = tokenizer.encode_plus(question,
text,
return_tensors='pt',
max_length=512)
input_ids = input_dict["input_ids"].tolist()
start_scores, end_scores = model(**input_dict)
start = torch.argmax(start_scores)
end = torch.argmax(end_scores)
all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
answer = ''.join(all_tokens[start:end + 1]).replace('▁', ' ').strip()
answer = answer.replace('[SEP]', '')
return answer if answer != '[CLS]' and len(
answer) != 0 else 'could not find an answer'
