def load_model():
model_dir = '../../model/model/'
config = BertConfig(num_labels=3, output_attentions=True)
config.from_pretrained('../../model/bert-cased/')
model = BertAttn(config,
option='feed',
dropout=0.1,
gpu=False,
seed=0,
do_lower_case=False)
class_weights = [0.6058, 0.1161, 0.2781]
model.set_focal_loss(alpha=class_weights, gamma=-1)
model.load_model(True, model_dir)
return model
def createCsvData():
config = BertConfig.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel(config)
with Cd("lemmadata"):
with open("id_to_sent.json") as sent_id_dict_file:
sent_id_dict = json.load(sent_id_dict_file)
for dir_item in os.listdir():
if os.path.isfile(dir_item):
if dir_item.endswith(".json") and dir_item != "id_to_sent.json":
print(dir_item)
with open(dir_item, "r") as f:
lemma_data = json.load(f)
with Cd("vectors"):
with open(dir_item[:-5]+".csv", "w") as vector_file:
writer = csv.writer(vector_file, delimiter=",")
for instance in lemma_data:
inst_sent_id = instance["sent_id"]
inst_sense = instance["sense"]
inst_sent = sent_id_dict[str(inst_sent_id)]
if(len(inst_sent) > 511):
continue
vector = vectorizeWordInContext(inst_sent, instance["pos"], tokenizer, model)
vec_list = vector.detach().tolist()
row_data = [inst_sent_id, instance["pos"], inst_sense] + vec_list
writer.writerow(row_data)
def main():
bert_base_config = BertConfig.from_pretrained('bert-base-uncased', num_labels=2)
bert_base_model = BertForSequenceClassification.from_pretrained('bert-base-uncased', config=bert_base_config)
count = 0
for name, param in bert_base_model.named_parameters():
if param.requires_grad:
size = 1
for s in param.data.size():
size = s * size
count += size
print('The total number of parameters in bert_base_uncased: ', count)
roberta_config = RobertaConfig.from_pretrained('roberta-base', num_labels=2)
roberta_model = RobertaForSequenceClassification.from_pretrained('roberta-base',config=roberta_config)
count = 0
for name, param in roberta_model.named_parameters():
if param.requires_grad:
size = 1
for s in param.data.size():
size = s * size
count += size
print('The total number of parameters in roberta: ', count)
albert_config = AlbertConfig.from_pretrained('albert-base-v2', num_labels=2)
albert_model = AlbertForSequenceClassification.from_pretrained('albert-base-v2', config=albert_config)
count = 0
for name, param in albert_model.named_parameters():
if param.requires_grad:
size = 1
for s in param.data.size():
size = s * size
count += size
print('The total number of parameters in albert: ', count)
def __init__(self, name='bert-base-uncased', dropout=0.1, num_class=2):
super(BertC, self).__init__()
config = BertConfig.from_pretrained(name)
self.bert = BertModel_attack(config)
self.proj = nn.Linear(config.hidden_size, num_class)
self.loss_f = nn.CrossEntropyLoss()
self.drop = nn.Dropout(p=dropout)
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, 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):
super(Bert, self).__init__()
self.tokenizer = BertTokenizer.from_pretrained(os.path.join(config.get('model_config')['language_model_path'], 'bert-base-uncased-vocab.txt'))
modelConfig = BertConfig.from_pretrained(os.path.join(config.get('model_config')['language_model_path'], 'bert_config.json'))
self.textExtractor = BertModel.from_pretrained(
os.path.join(config.get('model_config')['language_model_path'], 'pytorch_model.bin'), config=modelConfig)
def __init__(self, vocab_size, tag_to_ix, hidden_dim, n_layers):
super(BERT_BiLSTM_CRF, self).__init__()
self.hidden_dim = hidden_dim
self.n_layers = n_layers
self.vocab_size = vocab_size
self.tag_to_ix = tag_to_ix
self.tagset_size = len(tag_to_ix)
config = BertConfig.from_pretrained('bert-base-multilingual-cased')
self.model = BertModel(config)
self.lstm = nn.LSTM(768,
hidden_dim,
num_layers=n_layers,
bidirectional=True)
# Maps the output of the LSTM into tag space.
self.hidden2tag = nn.Linear(hidden_dim * 2, self.tagset_size)
# Matrix of transition parameters. Entry i,j is the score of
# transitioning *to* i *from* j.
self.transitions = nn.Parameter(
torch.randn(self.tagset_size, self.tagset_size, device=device))
# These two statements enforce the constraint that we never transfer
# to the start tag and we never transfer from the stop tag
self.transitions.data[tag_to_ix[START_TAG], :] = -10000
self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000
self.hidden = self.init_hidden()
def load_model(model_name: str, do_lower_case=False):
config = BertConfig.from_pretrained(model_name)
tokenizer = BertTokenizer.from_pretrained(model_name,
do_lower_case=do_lower_case)
model = BertForQuestionAnswering.from_pretrained(model_name,
from_tf=False,
config=config)
return model, tokenizer
def __init__(self, hidden_dim, n_layers, tagset_size):
super(BertLSTM, self).__init__()
config = BertConfig.from_pretrained('bert-base-multilingual-cased')
self.model = BertModel(config)
self.decoder = nn.LSTM(768, hidden_dim, n_layers)
self.hiddentotag = nn.Linear(hidden_dim, tagset_size)
def load_model(self, model_path: str, do_lower_case=False):
config = BertConfig.from_pretrained(model_path + "/config.json")
tokenizer = BertTokenizer.from_pretrained(model_path,
do_lower_case=do_lower_case)
model = BertForQuestionAnswering.from_pretrained(model_path,
from_tf=False,
config=config)
return model, tokenizer
def start_inference(data, dialogue_type, dest, batchsize, bert_model, cuda):
assert torch.cuda.is_available(
) == True, 'PyTorch not running on GPU! #sadpanda'
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.manual_seed(100)
dialogue_type_dict = {'DB': 'db_response_new', 'normal': 'response'}
config = BertConfig.from_pretrained(bert_model)
tokenizer = BertTokenizer.from_pretrained(bert_model)
model = BertForNextSentencePrediction(config)
model.cuda()
model.eval()
df = pd.read_csv(data, usecols=['id'])
df.dropna(inplace=True)
row_count = df.shape[0]
del df
chunk_count = math.ceil(row_count / batchsize)
with open(dest, 'w+'):
pass
cols = ['context', dialogue_type_dict[dialogue_type]]
for i, chunk in enumerate(
tqdm(pd.read_csv(open(data, 'r'),
usecols=cols,
chunksize=batchsize),
desc='Batches',
total=chunk_count)):
samples = get_batch(chunk, dialogue_type_dict[dialogue_type])
assert len(samples) == chunk.shape[0], 'Some samples went missing!'
if batchsize == 1:
results = convert_single_example_to_features(samples, tokenizer)
else:
results = convert_examples_to_features(samples, tokenizer)
with torch.no_grad():
input_ids = torch.tensor([x.input_ids for x in results]).cuda()
token_type_ids = torch.tensor([x.input_type_ids
for x in results]).cuda()
attention_mask = torch.tensor([x.input_mask
for x in results]).cuda()
outputs = model(input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask)[0]
outputs = torch.softmax(outputs, dim=1)
db_probs = outputs[:, 1]
with open(dest, 'a') as f:
f.write('\n'.join([str(x) for x in db_probs.tolist()]) + '\n')
def main():
torch.cuda.empty_cache()
parser = setup_parser()
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.")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
args.n_gpu = torch.cuda.device_count()
args.device = device
set_seed(args)
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: {}".format(args.task_name))
processor = processors[args.task_name]()
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels()
##Load Models
config = BertConfig.from_pretrained(args.config_name)
tokenizer = BertTokenizer.from_pretrained(args.text_encoder_checkpoint,
do_lower_case=args.do_lower_case)
text_encoder = BertModel.from_pretrained(args.text_encoder_checkpoint,
config=config)
graph_encoder = GraphEncoder(args.n_hidden, args.min_score)
medsts_classifier = PairClassifier(config.hidden_size + args.n_hidden, 1)
medsts_c_classifier = PairClassifier(config.hidden_size + args.n_hidden, 5)
medsts_c2_classifier = PairClassifier(config.hidden_size + args.n_hidden,
2)
medsts_type_classifier = PairClassifier(config.hidden_size + args.n_hidden,
4)
model = MedstsNet(text_encoder, graph_encoder, medsts_classifier,
medsts_c_classifier, medsts_c2_classifier,
medsts_type_classifier)
if args.text_only:
medsts_classifier = PairClassifier(config.hidden_size, 1)
medsts_c_classifier = PairClassifier(config.hidden_size, 5)
medsts_c2_classifier = PairClassifier(config.hidden_size, 2)
medsts_type_classifier = PairClassifier(config.hidden_size, 4)
model = MedstsNet_Textonly(text_encoder, medsts_classifier,
medsts_c_classifier, medsts_c2_classifier,
medsts_type_classifier)
model.to(args.device)
args.n_gpu = 1
if args.do_train:
train_dataset = load_and_cache_examples(args,
args.task_name,
tokenizer,
evaluate=False,
reverse=True)
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info('global step = {}, average loss = {}'.format(
global_step, tr_loss))
def main():
torch.cuda.empty_cache()
parser = setup_parser()
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.")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
args.n_gpu = torch.cuda.device_count()
args.device = device
set_seed(args)
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: {}".format(args.task_name))
processor = processors[args.task_name]()
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
##Load Models
config = BertConfig.from_pretrained(args.config_name)
tokenizer = BertTokenizer.from_pretrained(args.text_encoder_checkpoint,
do_lower_case=args.do_lower_case)
text_encoder = BertModel.from_pretrained(args.text_encoder_checkpoint,
config=config)
graph_encoder = GraphEncoder(args.n_hidden, args.min_score)
if args.graph_encoder_checkpoint:
graph_encoder.gcnnet.load_state_dict(
torch.load(args.graph_encoder_checkpoint))
medsts_classifier = PairClassifier(config.hidden_size + args.n_hidden, 1)
medsts_c_classifier = PairClassifier(config.hidden_size + args.n_hidden, 5)
medsts_type_classifier = PairClassifier(config.hidden_size + args.n_hidden,
4)
model = MedstsNet(text_encoder, graph_encoder, medsts_classifier,
medsts_c_classifier, medsts_type_classifier)
model.to(args.device)
args.n_gpu = 1
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 = {}, average loss = {}'.format(
global_step, tr_loss))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
logger.info("saving model checkpoint to {}".format(args.output_dir))
model_to_save = model.module if hasattr(model, 'module') else model
# model_to_save.save_pretrained(args.output_dir)
torch.save(model_to_save.state_dict(),
os.path.join(args.output_dir, 'saved_model.pth'))
tokenizer.save_pretrained(args.output_dir)
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
def __init__(self, code_length): # code_length为fc映射到的维度大小
super(TextNet, self).__init__()
modelConfig = BertConfig.from_pretrained(
'./data/bert-base-uncased-config.json')
self.textExtractor = BertModel.from_pretrained(
'./data/bert-base-uncased-pytorch_model.bin', config=modelConfig)
# self.textExtractor.eval()
embedding_dim = self.textExtractor.config.hidden_size
def load_artifacts(model_path):
""" Loads pretrained model , tokenizer , config."""
model_class = BertForQuestionAnswering
model = model_class.from_pretrained(model_path)
tokenizer = BertTokenizer.from_pretrained(model_path)
config = BertConfig.from_pretrained(model_path)
model.to("cpu")
model.eval()
return model, tokenizer, config
def load_artifacts(model_path):
""" Loads pretrained model , tokenizer , config."""
model_class = BertForSequenceClassification
model = model_class.from_pretrained(model_path)
tokenizer = BertTokenizer.from_pretrained(model_path)
config = BertConfig.from_pretrained(model_path)
model.to("cpu")
model.eval()
return model, tokenizer, config
def __init__(self, code_length=1024):
super(TextNet, self).__init__()
modelConfig = BertConfig.from_pretrained(
'/home/hengyuli/cross-modal/model/bert_config.json')
self.textExtractor = BertModel.from_pretrained(
'/home/hengyuli/cross-modal/model/pytorch_model.bin',
config=modelConfig)
embedding_dim = self.textExtractor.config.hidden_size
self.fc = nn.Linear(embedding_dim, code_length)
self.tanh = torch.nn.Tanh()
def __init__(self):
super(Bert, self).__init__()
self.tokenizer = BertTokenizer.from_pretrained(
'../pretrained/bert-base-uncased/bert-base-uncased-vocab.txt')
modelConfig = BertConfig.from_pretrained(
'../pretrained/bert-base-uncased/bert_config.json')
self.textExtractor = BertModel.from_pretrained(
'../pretrained/bert-base-uncased/pytorch_model.bin',
config=modelConfig)
def __init__(self, code_length): # code_length为fc映射到的维度大小
super(TextNet, self).__init__()
modelConfig = BertConfig.from_pretrained('bert-base-chinese')
self.textExtractor = BertModel.from_pretrained('bert-base-chinese',
config=modelConfig)
embedding_dim = self.textExtractor.config.hidden_size #embedding_dim应该是模型截断处输出的维度
self.fc = nn.Linear(embedding_dim, code_length)
self.tanh = torch.nn.Tanh()
def __init__(self, opt):
self.opt = opt
if opt.model_name.lower() in ['vh_bert', 'bert_att', 'my_lcf']:
tokenizer = BertTokenizer.from_pretrained(opt.pretrained_bert_name)
config = BertConfig.from_pretrained(opt.pretrained_bert_name,
output_attentions=True)
self.model = opt.model_class(config, ).to(opt.device)
elif 'bert' in opt.model_name.lower():
tokenizer = Tokenizer4Bert(opt.max_seq_len,
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.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='./cache/{0}_tokenizer.dat'.format(opt.dataset))
embedding_matrix = build_embedding_matrix(
word2idx=tokenizer.word2idx,
embed_dim=opt.embed_dim,
dat_fname='./cache/{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 build_model(do_lower_case, num_labels):
config = BertConfig.from_pretrained(MODEL_NAME,
num_labels=num_labels,
output_hidden_states=True)
tokenizer = TOKENIZER_CLASS.from_pretrained(MODEL_NAME,
do_lower_case=do_lower_case)
model = MODEL_CLASS.from_pretrained(MODEL_NAME, config=config)
model.to(get_device())
return model, tokenizer
def __init__(self, code_length): # code_length为fc映射到的维度大小
super(TextNet, self).__init__()
# model_name = 'bert-base-multilingual-cased'
modelConfig = BertConfig.from_pretrained(model_name)
self.textExtractor = BertModel.from_pretrained(
model_name, config=modelConfig)
embedding_dim = self.textExtractor.config.hidden_size #embedding_dim是模型截断处输出的维度
self.fc = nn.Linear(embedding_dim, code_length) # code_length是特征维度
self.tanh = torch.nn.Tanh()
def __init__(self, model_name: str) -> None:
super().__init__()
config = BertConfig.from_pretrained(model_name)
self.input_dim = config.hidden_size
self.output_dim = config.vocab_size
# TODO(mattg): It's possible that we could use some kind of cache like we have in
# allennlp.modules.token_embedders.bert_token_embedder.PretrainedBertModel. That way, we
# would only load the BERT weights once. Though, it's not clear how to do that here, as we
# need to load `BertForMaskedLM`, not just `BertModel`...
bert_model = BertForMaskedLM.from_pretrained(model_name)
self.bert_lm_head = bert_model.cls # pylint: disable=no-member
def __init__(self, model_state_dict) -> None:
no_cuda = True
self.device = torch.device(
"cuda" if torch.cuda.is_available() and not no_cuda else "cpu")
self.tokenizer = BertTokenizer.from_pretrained('bert-base-chinese',
do_lower_case=False)
config = BertConfig.from_pretrained('bert-base-chinese')
self.model = BertForQuestionAnswering(config)
self.model.load_state_dict(
torch.load(model_state_dict, map_location='cpu'))
self.model.to(self.device)
self.model.eval() # TODO
def __init__(self, code_length):
super(TextNet, self).__init__()
modelConfig = BertConfig.from_pretrained(
'/home/disk1/zhaoyuying/models/modeling_bert/bert-base-uncased-config.json'
)
self.textExtractor = BertModel.from_pretrained(
'/home/disk1/zhaoyuying/models/modeling_bert/bert-base-uncased-pytorch_model.bin',
config=modelConfig)
embedding_dim = self.textExtractor.config.hidden_size
self.fc = nn.Linear(embedding_dim, code_length)
self.tanh = torch.nn.Tanh()
def load_pretrained_model(model_path: str, lower_case=True):
"""
Imports pretrained BERT model from the official format as seen on:
https://github.com/google-research/bert
:param model_path: Path to the model checkpoint file
:param lower_case: select False if loading cased model
:return: pretrained model and its tokenizer
"""
config = BertConfig.from_pretrained(model_path + "/bert_config.json")
tokenizer = BertTokenizer.from_pretrained(model_path, do_lower_case=lower_case)
model = BertForQuestionAnswering.from_pretrained(model_path, from_tf=False, config=config)
return model, tokenizer
def init(maxlen=512):
global config, tokenizer, model, sim_model, MAX_LENGTH
MAX_LENGTH = maxlen
bert_model_name = 'bert-base-uncased'
config = BertConfig.from_pretrained(bert_model_name)
config.output_hidden_states = True
tokenizer = BertTokenizer.from_pretrained(bert_model_name)
model = BertForMaskedLM.from_pretrained(bert_model_name, config=config)
model.to(DEVICE)
model.eval()
sim_model = smodel.WebBertSimilarity(device=DEVICE)
def train(
root=True,
binary=False,
bert="bert-large-uncased",
epochs=30,
batch_size=8,
save=False,
):
trainset = SSTDataset("train", root=root, binary=binary)
devset = SSTDataset("dev", root=root, binary=binary)
testset = SSTDataset("test", root=root, binary=binary)
config = BertConfig.from_pretrained(bert)
if not binary:
config.num_labels = 5
model = BertForSequenceClassification.from_pretrained(bert, config=config)
model = model.to(device)
lossfn = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
for epoch in range(1, epochs):
train_loss, train_acc = train_one_epoch(model,
lossfn,
optimizer,
trainset,
batch_size=batch_size)
val_loss, val_acc = evaluate_one_epoch(model,
lossfn,
optimizer,
devset,
batch_size=batch_size)
test_loss, test_acc = evaluate_one_epoch(model,
lossfn,
optimizer,
testset,
batch_size=batch_size)
logger.info(f"epoch={epoch}")
logger.info(
f"train_loss={train_loss:.4f}, val_loss={val_loss:.4f}, test_loss={test_loss:.4f}"
)
logger.info(
f"train_acc={train_acc:.3f}, val_acc={val_acc:.3f}, test_acc={test_acc:.3f}"
)
if save:
label = "binary" if binary else "fine"
nodes = "root" if root else "all"
torch.save(model, f"{bert}__{nodes}__{label}__e{epoch}.pickle")
logger.success("Done!")
def load_artifacts(model_path, is_quantized=False):
""" Loads pretrained model , tokenizer , config."""
model_class = BertForSequenceClassification
print("quantized_ouput/" if is_quantized else model_path)
if not is_quantized:
model = model_class.from_pretrained(model_path)
else:
model = torch.load("4bit_quantized_model.bin")
tokenizer = BertTokenizer.from_pretrained(model_path)
config = BertConfig.from_pretrained(
"quantized_ouput/" if is_quantized else model_path)
model.to("cpu")
model.eval()
return model, tokenizer, config
