def __init__(self, large, temp_dir, finetune=False):
super(Bert, self).__init__()
if(large):
self.model = BertModel.from_pretrained('bert-large-uncased', cache_dir=temp_dir)
else:
self.model = BertModel.from_pretrained('bert-base-uncased', cache_dir=temp_dir)
self.finetune = finetune
def __init__(self, model_path, large, temp_dir, finetune=False):
super(Bert, self).__init__()
if(large):
self.model = BertModel.from_pretrained(model_path)
else:
self.model = BertModel.from_pretrained(model_path)
self.finetune = finetune
def __init__(self, temp_dir, cased=False, finetune=False):
super(Bert, self).__init__()
if (cased):
self.model = BertModel.from_pretrained('BETO', cache_dir=temp_dir)
else:
self.model = BertModel.from_pretrained('BETO', cache_dir=temp_dir)
self.finetune = finetune
def __init__(self, large, temp_dir, finetune=False):
super(Bert, self).__init__()
if (large):
self.model = BertModel.from_pretrained(BERT_PATH,
cache_dir=temp_dir)
else:
self.model = BertModel.from_pretrained(BERT_PATH,
cache_dir=temp_dir)
self.finetune = finetune
def __init__(self, device, config, labels=None):
super().__init__()
if config.model == "BertCased":
self.bert = BertModel.from_pretrained('bert-base-cased', output_hidden_states=True)
else:
self.bert = BertModel.from_pretrained('bert-base-uncased', output_hidden_states=True)
self.fc = nn.Linear(768*12, labels).to(device)
self.device = device
def __init__(self, large, temp_dir, finetune=False):
super(Bert, self).__init__()
if (large):
self.model = BertModel.from_pretrained('bert-large-uncased',
cache_dir=temp_dir)
else:
self.model = BertModel.from_pretrained(
"/content/PhoBERT_base_transformers", cache_dir=temp_dir)
self.finetune = finetune
def __init__(self, large, temp_dir, finetune=False):
super(Bert, self).__init__()
if (large):
self.model = BertModel.from_pretrained('bert-large-uncased',
cache_dir=temp_dir)
else:
self.model = BertModel.from_pretrained(
'/home/ybai/projects/PreSumm/PreSumm/temp/',
cache_dir=temp_dir)
self.finetune = finetune
def __init__(self, large, temp_dir, finetune=False):
super(Bert, self).__init__()
if(large):
# self.model = BertModel.from_pretrained('bert-large-uncased', cache_dir=temp_dir)
self.model = BertModel.from_pretrained(temp_dir, cache_dir='~/.cache/torch/pytorch_transformers')
else:
# self.model = BertModel.from_pretrained('bert-base-uncased', cache_dir=temp_dir)
self.model = BertModel.from_pretrained(temp_dir, cache_dir='~/.cache/torch/pytorch_transformers')
self.finetune = finetune
def __init__(self, bert_model, temp_dir, finetune=False):
super(Bert, self).__init__()
if (bert_model == 'bert-base-multilingual-cased'):
self.model = BertModel.from_pretrained(
'bert-base-multilingual-cased', cache_dir=temp_dir)
else:
self.model = BertModel.from_pretrained(bert_model,
cache_dir=temp_dir)
self.model.resize_token_embeddings(31756)
self.word_embeddings = nn.Embedding(31756, 768, padding_idx=0)
self.finetune = finetune
def __init__(self, large, temp_dir, finetune=False):
super(Bert, self).__init__()
if (large):
self.model = BertModel.from_pretrained('bert-large-uncased',
cache_dir=temp_dir)
else:
self.model = BertModel.from_pretrained(
'bert-base-chinese',
cache_dir=
r'C:\Users\Administrator\PycharmProjects\one\bertsum-chinese-LAI\temp'
)
self.finetune = finetune #ture
def __init__(self, large, temp_dir, finetune=False):
super(Bert, self).__init__()
if (large):
#self.model = BertModel.from_pretrained('bert-base-multilingual-cased', cache_dir=temp_dir)
config = BertConfig.from_json_file('bert-large/config.json')
self.model = BertModel.from_pretrained('bert-large',
cache_dir=None,
config=config)
else:
self.model = BertModel.from_pretrained(
'bert-base-multilingual-cased', cache_dir=temp_dir)
self.finetune = finetune
def __init__(self, device='cpu', language='en', labels=None):
super().__init__()
if language == 'en':
self.bert = BertModel.from_pretrained('bert-base-cased')
else:
self.bert = BertModel.from_pretrained('bert-base-chinese')
self.fc = nn.Linear(1536, 4)
self.device = device
self.lstm = nn.LSTM(input_size=768,
hidden_size=768,
num_layers=1,
dropout=0,
bidirectional=True)
def __init__(self, data, args, num_episodes=None):
self.data = data
self.args = args
self.num_episodes = num_episodes
self.all_classes = np.unique(self.data['label'])
self.num_classes = len(self.all_classes)
if self.num_classes < self.args.way:
raise ValueError("Total number of classes is less than #way.")
self.idx_list = []
for y in self.all_classes:
self.idx_list.append(
np.squeeze(np.argwhere(self.data['label'] == y)))
self.count = 0
self.done_queue = Queue()
self.num_cores = cpu_count() if args.n_workers is 0 else args.n_workers
# print("{}, Initializing parallel data loader with {} processes".format(
# datetime.datetime.now().strftime('%02y/%02m/%02d %H:%M:%S'),
# self.num_cores), flush=True)
if self.args.bert_path is not None:
# use bert online
print("{}, Loading pretrained bert from {}".format(
datetime.datetime.now().strftime('%02y/%02m/%02d %H:%M:%S'),
self.args.bert_path), flush=True)
if self.args.cuda != -1:
self.model = BertModel.from_pretrained(
'bert-base-uncased',
cache_dir=self.args.bert_path).cuda(self.args.cuda)
else:
self.model = BertModel.from_pretrained(
'bert-base-uncased',
cache_dir=self.args.bert_path)
self.model.eval()
self.p_list = []
for i in range(self.num_cores):
self.p_list.append(
Process(target=self.worker, args=(self.done_queue,)))
for i in range(self.num_cores):
self.p_list[i].start()
def main(raw_args=None):
parser = argparse.ArgumentParser()
parser.add_argument("--model_name",
type=str,
required=True,
help="model name e.g. bert-base-uncased")
parser.add_argument("--cache_dir",
type=str,
default=None,
required=False,
help="Directory containing pytorch model")
parser.add_argument("--pytorch_model_path",
type=str,
required=True,
help="/path/to/<pytorch-model-name>.bin")
parser.add_argument("--tf_cache_dir",
type=str,
required=True,
help="Directory in which to save tensorflow model")
args = parser.parse_args(raw_args)
model = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name,
state_dict=torch.load(args.pytorch_model_path),
cache_dir=args.cache_dir
)
convert_pytorch_checkpoint_to_tf(
model=model,
ckpt_dir=args.tf_cache_dir,
model_name=args.model_name
)
def __init__(self, opt):
self.opt = opt
if 'bert' in opt.model_name:
tokenizer = Tokenizer4Bert(opt.max_seq_len,
opt.pretrained_bert_name)
bert = BertModel.from_pretrained(opt.pretrained_bert_name)
self.model = opt.model_class(bert, opt).to(opt.device)
else:
tokenizer = build_tokenizer(
fnames=[opt.dataset_file['train'], opt.dataset_file['test']],
max_seq_len=opt.max_seq_len,
dat_fname='{0}_tokenizer.dat'.format(opt.dataset))
embedding_matrix = build_embedding_matrix(
word2idx=tokenizer.word2idx,
embed_dim=opt.embed_dim,
dat_fname='{0}_{1}_embedding_matrix.dat'.format(
str(opt.embed_dim), opt.dataset))
self.model = opt.model_class(embedding_matrix, opt).to(opt.device)
self.trainset = ABSADataset(opt.dataset_file['train'], tokenizer)
self.testset = ABSADataset(opt.dataset_file['test'], tokenizer)
assert 0 <= opt.valset_ratio < 1
if opt.valset_ratio > 0:
valset_len = int(len(self.trainset) * opt.valset_ratio)
self.trainset, self.valset = random_split(
self.trainset, (len(self.trainset) - valset_len, valset_len))
else:
self.valset = self.testset
if opt.device.type == 'cuda':
logger.info('cuda memory allocated: {}'.format(
torch.cuda.memory_allocated(device=opt.device.index)))
self._print_args()
def __init__(self, opt):
self.opt = opt
if 'bert' in opt.model_name:
tokenizer = Tokenizer4Bert(opt.max_seq_len,
opt.pretrained_bert_name)
# bert = BertModel.from_pretrained(opt.pretrained_bert_name)
config = BertConfig.from_pretrained(opt.pretrained_bert_name,
output_attentions=True)
bert = BertModel.from_pretrained(opt.pretrained_bert_name,
config=config)
self.pretrained_bert_state_dict = bert.state_dict()
self.model = opt.model_class(bert, opt).to(opt.device)
else:
tokenizer = build_tokenizer(
fnames=[opt.dataset_file['train'], opt.dataset_file['test']],
max_seq_len=opt.max_seq_len,
dat_fname='{0}_tokenizer.dat'.format(opt.dataset))
embedding_matrix = build_embedding_matrix(
word2idx=tokenizer.word2idx,
embed_dim=opt.embed_dim,
dat_fname='{0}_{1}_embedding_matrix.dat'.format(
str(opt.embed_dim), opt.dataset))
self.model = opt.model_class(embedding_matrix, opt).to(opt.device)
self.trainset = ABSADataset(opt.dataset_file['train'], tokenizer)
self.testset = ABSADataset(opt.dataset_file['test'], tokenizer)
if opt.device.type == 'cuda':
logger.info('cuda memory allocated: {}'.format(
torch.cuda.memory_allocated(device=opt.device.index)))
self._print_args()
def __init__(self, num_labels=2):
super(BertForSequenceClassification, self).__init__()
self.num_labels = num_labels
self.bert = BertModel.from_pretrained('bert-base-uncased')
self.dropout = nn.Dropout(dropout_prob)
self.classifier = nn.Linear(hidden_size, num_labels)
nn.init.xavier_normal_(self.classifier.weight)
def __init__(
self,
bert_model_or_path: str = "bert-base-uncased",
layers: str = "-1,-2,-3,-4",
pooling_operation: str = "first",
use_scalar_mix: bool = False,
):
"""
Bidirectional transformer embeddings of words, as proposed in Devlin et al., 2018.
:param bert_model_or_path: name of BERT model ('') or directory path containing custom model, configuration file
and vocab file (names of three files should be - config.json, pytorch_model.bin/model.chkpt, vocab.txt)
:param layers: string indicating which layers to take for embedding
:param pooling_operation: how to get from token piece embeddings to token embedding. Either pool them and take
the average ('mean') or use first word piece embedding as token embedding ('first)
"""
super().__init__()
self.tokenizer = BertTokenizer.from_pretrained(bert_model_or_path)
self.model = BertModel.from_pretrained(
pretrained_model_name_or_path=bert_model_or_path,
output_hidden_states=True)
self.layer_indexes = [int(x) for x in layers.split(",")]
self.pooling_operation = pooling_operation
self.use_scalar_mix = use_scalar_mix
self.name = str(bert_model_or_path)
self.static_embeddings = True
self.model.to(flair.device)
self.model.eval()
def __init__(self, config: BertEncoderConfig = BertEncoderConfig()):
assert isinstance(config, BertEncoderConfig)
super().__init__(config)
self.config = config
self.model = BertModel.from_pretrained(self.config.mode,
output_hidden_states=True)
self.freeze_layers()
def __init__(self, rnn_type, bert_model_path, bert_embedding_dim,
hidden_dim, n_layers, bidirectional, batch_first, dropout,
num_classes):
super(BertRNN, self).__init__()
self.rnn_type = rnn_type.lower()
self.bidirectional = bidirectional
self.hidden_dim = hidden_dim
self.n_layers = n_layers
self.bert = BertModel.from_pretrained(bert_model_path)
for param in self.bert.parameters():
param.requires_grad = True
if rnn_type == 'lstm':
self.rnn = nn.LSTM(bert_embedding_dim,
hidden_size=hidden_dim,
num_layers=n_layers,
bidirectional=bidirectional,
batch_first=batch_first,
dropout=dropout)
elif rnn_type == 'gru':
self.rnn = nn.GRU(bert_embedding_dim,
hidden_size=hidden_dim,
num_layers=n_layers,
bidirectional=bidirectional,
batch_first=batch_first,
dropout=dropout)
else:
self.rnn = nn.RNN(bert_embedding_dim,
hidden_size=hidden_dim,
num_layers=n_layers,
bidirectional=bidirectional,
batch_first=batch_first,
dropout=dropout)
self.dropout = nn.Dropout(dropout)
self.fc_rnn = nn.Linear(hidden_dim * 2, num_classes)
def get_bert(bert_type='bert'):
tokenizer, model = None, None
if (bert_type == 'bert'):
######## bert ###########
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
########################
if (bert_type == 'biobert'):
#### Bio BERT #########
model = bm.from_pretrained('biobert_v1.1_pubmed')
tokenizer = BertTokenizer(vocab_file="biobert_v1.1_pubmed/vocab.txt",
do_lower_case=True)
#### Bio BERT #########
if (bert_type == 'scibert'):
#### sci bert #########
config = AutoConfig.from_pretrained('allenai/scibert_scivocab_uncased',
output_hidden_states=False)
tokenizer = AutoTokenizer.from_pretrained(
'allenai/scibert_scivocab_uncased')
model = AutoModel.from_pretrained('allenai/scibert_scivocab_uncased',
config=config)
#######################
return tokenizer, model
def __init__(self, opt, model_path: str):
super(ModelTrained, self).__init__()
self.opt = opt
self.model_path = model_path
bert = BertModel.from_pretrained(opt.pretrained_bert_name)
self.model = opt.model_class(bert, opt).to(opt.device)
self.model.load_state_dict(torch.load(model_path))
def load_bert_to_cache(cache: Dict):
bert_model_name = 'bert-base-multilingual-cased'
if 'bert_tokenizer' not in cache:
cache['bert_tokenizer'] = BertTokenizer.from_pretrained(
bert_model_name)
if 'bert_model' not in cache:
cache['bert_model'] = BertModel.from_pretrained(bert_model_name)
def __init__(self, bert_model_path, large, temp_dir, finetune=False):
super(Bert, self).__init__()
self.model_name = 'bert-large-uncased' if large else 'bert-base-uncased'
self.model = BertModel.from_pretrained(path.join(
bert_model_path, self.model_name),
cache_dir=temp_dir)
self.finetune = finetune
def __init__(self):
model_classes = {
# 'bert_spc': BERT_SPC,
# 'aen_bert': AEN_BERT,
'lcf_bert': LCF_BERT
}
# set your trained models here
state_dict_paths = {
'lcf_bert': 'models/lcf_bert_combined_val_acc0.7563',
# 'lcf_bert': 'models/lcf_bert_twitter_val_acc0.7283',
# 'lcf_bert': 'models/lcf_bert_twitter_val_acc0.7225',
# 'bert_spc': 'state_dict/bert_spc_laptop_val_acc0.268',
# 'aen_bert': 'state_dict/aen_bert_laptop_val_acc0.2006'
}
self.opt = get_parameters()
self.opt.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# self.opt.device = torch.device("cpu")
print("TargetSentimentAnalyzer using device:",self.opt.device)
self.tokenizer = Tokenizer4Bert(self.opt.max_seq_len, self.opt.pretrained_bert_name)
self.bert = BertModel.from_pretrained(self.opt.pretrained_bert_name)
self.model = model_classes[self.opt.model_name](self.bert, self.opt).to(self.opt.device)
print('loading model {0} ...'.format(self.opt.model_name))
self.model.load_state_dict(torch.load(state_dict_paths[self.opt.model_name], map_location=torch.device('cpu')))
# if torch.cuda.is_available():
# else:
# self.model.load_state_dict(torch.load(state_dict_paths[self.opt.model_name]))
self.model.eval()
torch.autograd.set_grad_enabled(False)
def __init__(self, server):
self.server = server
self.dimensions = 768
transformer_type = 'bert-base-uncased'
self.tokenizer = BertTokenizer.from_pretrained(transformer_type)
self.model = BertModel.from_pretrained(transformer_type)
self.model.eval()
def main():
training_dictionaries = load_training_dictionaries()
bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
bert_model = BertModel.from_pretrained('bert-base-uncased')
extractor_model = ExtractorModel(bert_tokenizer, bert_model)
# train_extractor(
# extractor_model,
# data=training_dictionaries
# )
abstractor_model = AbstractorModelRNN(bert_tokenizer, bert_model)
# train_abstractor(
# abstractor_model,
# data=training_dictionaries
# )
rl_model = RLModel(
extractor_model,
abstractor_model,
)
train_system(
rl_model=rl_model,
data=training_dictionaries
)
def __init__(self, args, temp_dir, finetune=False):
super(Bert, self).__init__()
if args.pretrained_model_type in ['bert-base-uncased', 'bert-base-multilingual-uncased']:
self.model = BertModel.from_pretrained(args.pretrained_model_type, cache_dir=temp_dir)
if args.pretrained_model_type in ['rubert-deeppavlov']:
name = args.pretrained_model_type
config = BertConfig.from_json_file(mapper(name, 'config'))
self.model = BertModel.from_pretrained(mapper(name, 'model'), config=config)
if not self.model:
raise NotImplementedError("self.model")
bert_data = BertData(args)
self.model.resize_token_embeddings(len(bert_data.tokenizer))
self.finetune = finetune
def __init__(self, args, dictionary, left_pad=False):
super().__init__(dictionary)
self.dropout = args.dropout
from pytorch_transformers import RobertaModel, BertModel
from pytorch_transformers.file_utils import PYTORCH_TRANSFORMERS_CACHE
from pytorch_transformers import RobertaConfig, RobertaTokenizer, BertConfig, BertTokenizer
if args.pretrained_bert_model.startswith('roberta'):
self.embed = RobertaModel.from_pretrained(
args.pretrained_bert_model,
cache_dir=PYTORCH_TRANSFORMERS_CACHE /
'distributed_{}'.format(args.distributed_rank))
# self.context = RobertaModel.from_pretrained(args.pretrained_bert_model,
# cache_dir=PYTORCH_TRANSFORMERS_CACHE / 'distributed_{}'.format(args.distributed_rank))
self.config = RobertaConfig.from_pretrained(
args.pretrained_bert_model)
self.tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
else:
self.embed = BertModel.from_pretrained(
args.pretrained_bert_model,
cache_dir=PYTORCH_TRANSFORMERS_CACHE /
'distributed_{}'.format(args.distributed_rank))
# self.context = BertModel.from_pretrained(args.pretrained_bert_model,
# cache_dir=PYTORCH_TRANSFORMERS_CACHE / 'distributed_{}'.format(args.distributed_rank))
self.config = BertConfig.from_pretrained(
args.pretrained_bert_model)
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
self.padding_idx = self.tokenizer.convert_tokens_to_ids(
self.tokenizer.pad_token)
def __init__(self, opt):
self.opt = opt
if 'bert' in opt.model_name:
tokenizer = Tokenizer4Bert(opt.max_seq_len,
opt.pretrained_bert_name)
bert = BertModel.from_pretrained(opt.pretrained_bert_name)
self.model = opt.model_class(bert, opt).to(opt.device)
else:
tokenizer = build_tokenizer(
fnames=[opt.dataset_file['train'], opt.dataset_file['test1']],
max_seq_len=opt.max_seq_len,
dat_fname='{0}_tokenizer.dat'.format(opt.dataset))
embedding_matrix = build_embedding_matrix(
word2idx=tokenizer.word2idx,
embed_dim=opt.embed_dim,
dat_fname='{0}_{1}_embedding_matrix.dat'.format(
str(opt.embed_dim), opt.dataset))
self.model = opt.model_class(embedding_matrix, opt).to(opt.device)
self.testset = ABSADataset(opt.dataset_file['test1'], tokenizer)
if opt.device.type == 'cuda':
logger.info('cuda memory allocated: {}'.format(
torch.cuda.memory_allocated(device=opt.device.index)))
model_path = 'state_dict/bert_spc_law_val_acc0.5314.hdf5' # provide best model path
self.model.load_state_dict(torch.load(model_path))
