def load_word_embedding_model(model_type,
task,
vocab_path,
word_tokenizer_class,
emb_path,
num_labels,
lower=True):
# Load config
config = BertConfig.from_pretrained('bert-base-uncased')
# Init word tokenizer
word_tokenizer = word_tokenizer_class()
# Load vocab
_, vocab_map = load_vocab(vocab_path)
tokenizer = SimpleTokenizer(vocab_map, word_tokenizer, lower=lower)
vocab_list = list(tokenizer.vocab.keys())
# Adjust config
if type(num_labels) == list:
config.num_labels = max(num_labels)
config.num_labels_list = num_labels
else:
config.num_labels = num_labels
config.num_hidden_layers = num_labels
if 'word2vec' in model_type:
embeddings = gen_embeddings(vocab_list, emb_path)
config.hidden_size = 400
config.num_attention_heads = 8
else: # 'fasttext'
embeddings = gen_embeddings(vocab_list, emb_path, emb_dim=300)
config.hidden_size = 300
config.num_attention_heads = 10
config.vocab_size = len(embeddings)
# Instantiate model
if 'sequence_classification' == task:
model = BertForSequenceClassification(config)
model.bert.embeddings.word_embeddings.weight.data.copy_(
torch.FloatTensor(embeddings))
elif 'token_classification' == task:
model = BertForWordClassification(config)
model.bert.embeddings.word_embeddings.weight.data.copy_(
torch.FloatTensor(embeddings))
elif 'multi_label_classification' == task:
model = BertForMultiLabelClassification(config)
model.bert.embeddings.word_embeddings.weight.data.copy_(
torch.FloatTensor(embeddings))
return model, tokenizer
def predict(text):
# Load Tokenizer and Config
tokenizer = BertTokenizer.from_pretrained('indobenchmark/indobert-base-p1')
config = BertConfig.from_pretrained('indobenchmark/indobert-base-p1')
config.num_labels = NerGritDataset.NUM_LABELS
# Instantiate model
w2i, i2w = NerGritDataset.LABEL2INDEX, NerGritDataset.INDEX2LABEL
model = BertForWordClassification.from_pretrained(
'indobenchmark/indobert-base-p1', config=config)
text = word_tokenize(text)
subwords, subword_to_word_indices = word_subword_tokenize(text, tokenizer)
subwords = torch.LongTensor(subwords).view(1, -1).to(model.device)
subword_to_word_indices = torch.LongTensor(subword_to_word_indices).view(
1, -1).to(model.device)
logits = model(subwords, subword_to_word_indices)[0]
preds = torch.topk(logits, k=1, dim=-1)[1].squeeze().numpy()
labels = [i2w[preds[i]] for i in range(len(preds))]
return pd.DataFrame({'words': text, 'label': labels})
def load_model(args):
if 'bert-base-multilingual' in args['model_checkpoint']:
# bert-base-multilingual-uncased or bert-base-multilingual-cased
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = BertTokenizer.from_pretrained(args['model_checkpoint'])
config = BertConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'token_classification' == args['task']:
model = BertForWordClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'xlm-mlm' in args['model_checkpoint']:
# xlm-mlm-100-1280
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = XLMTokenizer.from_pretrained(args['model_checkpoint'])
config = XLMConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = XLMForSequenceClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'token_classification' == args['task']:
model = XLMForWordClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'multi_label_classification' == args['task']:
model = XLMForMultiLabelClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'xlm-roberta' in args['model_checkpoint']:
# xlm-roberta-base or xlm-roberta-large
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = XLMRobertaTokenizer.from_pretrained(
args['model_checkpoint'])
config = XLMRobertaConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = XLMRobertaForSequenceClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'token_classification' == args['task']:
model = XLMRobertaForWordClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'multi_label_classification' == args['task']:
model = XLMRobertaForMultiLabelClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'fasttext' in args['model_checkpoint']:
# Prepare config & tokenizer
vocab_path = args['vocab_path']
config_path = None
word_tokenizer = args['word_tokenizer_class']()
emb_path = args['embedding_path'][args['model_checkpoint']]
_, vocab_map = load_vocab(vocab_path)
tokenizer = SimpleTokenizer(vocab_map,
word_tokenizer,
lower=args["lower"])
vocab_list = list(tokenizer.vocab.keys())
config = BertConfig.from_pretrained('bert-base-uncased')
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
config.num_hidden_layers = args["num_layers"]
embeddings = gen_embeddings(vocab_list, emb_path, emb_dim=300)
config.hidden_size = 300
config.num_attention_heads = 10
config.vocab_size = len(embeddings)
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
model.bert.embeddings.word_embeddings.weight.data.copy_(
torch.FloatTensor(embeddings))
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
model.bert.embeddings.word_embeddings.weight.data.copy_(
torch.FloatTensor(embeddings))
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
model.bert.embeddings.word_embeddings.weight.data.copy_(
torch.FloatTensor(embeddings))
elif 'scratch' in args['model_checkpoint']:
vocab_path, config_path = None, None
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
config = BertConfig.from_pretrained("bert-base-uncased")
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
config.num_hidden_layers = args["num_layers"]
config.hidden_size = 300
config.num_attention_heads = 10
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config=config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config=config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config=config)
elif 'indobenchmark' in args['model_checkpoint']:
# indobenchmark models
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = BertTokenizer.from_pretrained(args['model_checkpoint'])
config = BertConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
model_class = None
if 'sequence_classification' == args['task']:
model_class = AlbertForSequenceClassification if 'lite' in args[
'model_checkpoint'] else BertForSequenceClassification
elif 'token_classification' == args['task']:
model_class = AlbertForWordClassification if 'lite' in args[
'model_checkpoint'] else BertForWordClassification
elif 'multi_label_classification' == args['task']:
model_class = AlbertForMultiLabelClassification if 'lite' in args[
'model_checkpoint'] else BertForMultiLabelClassification
model = model_class.from_pretrained(args['model_checkpoint'],
config=config)
return model, tokenizer, vocab_path, config_path
def load_model(args):
if 'albert-large-wwmlm-512' == args['model_checkpoint']:
vocab_path = "../embeddings/albert-large-wwmlm-512/albert_large_model_bpe_wwmlm_512_vocab_uncased_30000.txt"
tokenizer = BertTokenizer(vocab_path)
config = AlbertConfig.from_json_file(
"../embeddings/albert-large-wwmlm-512/albert_large_model_bpe_wwmlm_512_albert_large_config.json"
)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = AlbertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = AlbertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = AlbertForMultiLabelClassification(config)
# Plug pretrained bert model
albert_model = AlbertModel.from_pretrained(
"../embeddings/albert-large-wwmlm-512/albert_large_model_bpe_wwmlm_512_pytorch_albert_large_512_629k.bin",
from_tf=False,
config=config)
model.albert = albert_model
elif 'albert-base-wwmlm-512' == args['model_checkpoint']:
vocab_path = "../embeddings/albert-base-wwmlm-512/albert_base_model_bpe_wwmlm_512_vocab_uncased_30000.txt"
config_path = "../embeddings/albert-base-wwmlm-512/albert_base_model_bpe_wwmlm_512_albert_base_config.json"
tokenizer = BertTokenizer(vocab_path)
config = AlbertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = AlbertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = AlbertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = AlbertForMultiLabelClassification(config)
# Plug pretrained bert model
albert_model = AlbertModel.from_pretrained(
"../embeddings/albert-base-wwmlm-512/albert_base_model_bpe_wwmlm_512_pytorch_model_albert_base_162k.bin",
from_tf=False,
config=config)
model.albert = albert_model
elif 'albert-large-wwmlm-128' == args['model_checkpoint']:
vocab_path = "../embeddings/albert-large-wwmlm-128/albert_large_model_bpe_wwmlm_128_vocab_uncased_30000.txt"
config_path = "../embeddings/albert-large-wwmlm-128/albert_large_model_bpe_wwmlm_128_albert_large_config.json"
tokenizer = BertTokenizer(vocab_path)
config = AlbertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = AlbertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = AlbertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = AlbertForMultiLabelClassification(config)
# Plug pretrained bert model
albert_model = AlbertModel.from_pretrained(
"../embeddings/albert-large-wwmlm-128/albert_large_model_bpe_wwmlm_128_pytorch_albert_large_128_500k.bin",
from_tf=False,
config=config)
model.albert = albert_model
elif 'babert-bpe-mlm-large-512' == args['model_checkpoint']:
# babert_bpe
# Prepare config & tokenizer
vocab_path = "../embeddings/babert-bpe-mlm-large-512/babert_model_bpe_mlm_uncased_large_512_dup10-5_vocab_uncased_30522.txt"
config_path = "../embeddings/babert-bpe-mlm-large-512/babert_model_bpe_mlm_uncased_large_512_dup10-5_bert_large_config.json"
tokenizer = BertTokenizer(vocab_path)
config = BertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
# Plug pretrained bert model
bert_model = BertForPreTraining.from_pretrained(
"../embeddings/babert-bpe-mlm-large-512/babert_model_bpe_mlm_uncased_large_512_dup10-5_pytorch_babert_uncased_large_512_dup10-5_1120k.bin",
config=config)
model.bert = bert_model.bert
elif 'albert-base-uncased-112500' == args['model_checkpoint']:
vocab_path = "../embeddings/albert-base-uncased-112500/vocab.txt"
config_path = "../embeddings/albert-base-uncased-112500/bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = AlbertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = AlbertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = AlbertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = AlbertForMultiLabelClassification(config)
# Plug pretrained bert model
albert_model = AlbertModel.from_pretrained(
"../embeddings/albert-base-uncased-112500/albert_base_uncased_112500.bin",
from_tf=False,
config=config)
model.albert = albert_model
elif 'albert-base-uncased-96000' == args['model_checkpoint']:
vocab_path = "../embeddings/albert-base-uncased-96000/vocab.txt"
config_path = "../embeddings/albert-base-uncased-96000/bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = AlbertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = AlbertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = AlbertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = AlbertForMultiLabelClassification(config)
# Plug pretrained bert model
albert_model = AlbertModel.from_pretrained(
"../embeddings/albert-base-uncased-96000/albert_base_uncased_96000.bin",
from_tf=False,
config=config)
model.albert = albert_model
elif 'albert-base-uncased-191k' == args['model_checkpoint']:
vocab_path = "../embeddings/albert-base-uncased-191k/pytorch_models_albert_base_uncased_191500_vocab_uncased_30000.txt"
config_path = "../embeddings/albert-base-uncased-191k/pytorch_models_albert_base_uncased_191500_albert_base_config.json"
tokenizer = BertTokenizer(vocab_path)
config = AlbertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = AlbertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = AlbertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = AlbertForMultiLabelClassification(config)
# Plug pretrained bert model
albert_model = AlbertModel.from_pretrained(
"../embeddings/albert-base-uncased-191k/pytorch_models_albert_base_uncased_191500_pytorch_model_albert_base_191k.bin",
from_tf=False,
config=config)
model.albert = albert_model
elif 'babert-opensubtitle' == args['model_checkpoint']:
# babert-opensubtitle
# Prepare config & tokenizer
vocab_path = "../embeddings/babert-opensubtitle/vocab.txt"
config_path = "../embeddings/babert-opensubtitle/bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = BertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
# Plug pretrained bert model
bert_model = BertForPreTraining.from_pretrained(
"../embeddings/babert-opensubtitle/model.ckpt-1000000.index",
from_tf=True,
config=config)
model.bert = bert_model.bert
elif 'babert-bpe-mlm-large-uncased-1100k' == args['model_checkpoint']:
# babert_bpe
# Prepare config & tokenizer
vocab_path = "../embeddings/babert-bpe-mlm-large-uncased-1100k/pytorch_models_babert_uncased_large_1100k_vocab_uncased_30522.txt"
config_path = "../embeddings/babert-bpe-mlm-large-uncased-1100k/pytorch_models_babert_uncased_large_1100k_bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = BertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
# Plug pretrained bert model
bert_model = BertForPreTraining.from_pretrained(
"../embeddings/babert-bpe-mlm-large-uncased-1100k/pytorch_models_babert_uncased_large_1100k_pytorch_model_babert_large_1100k.bin",
config=config)
model.bert = bert_model.bert
elif 'babert-bpe-mlm-large-uncased-1m' == args['model_checkpoint']:
# babert_bpe
# Prepare config & tokenizer
vocab_path = "../embeddings/babert-bpe-mlm-large-uncased-1m/pytorch_models_babert_uncased_large_1mil_vocab_uncased_30522.txt"
config_path = "../embeddings/babert-bpe-mlm-large-uncased-1m/pytorch_models_babert_uncased_large_1mil_bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = BertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
# Plug pretrained bert model
bert_model = BertForPreTraining.from_pretrained(
"../embeddings/babert-bpe-mlm-large-uncased-1m/pytorch_models_babert_uncased_large_1mil_pytorch_model_babert_large_1mil.bin",
config=config)
model.bert = bert_model.bert
elif 'babert-base-512' == args['model_checkpoint']:
# babert_bpe
# Prepare config & tokenizer
vocab_path = "../embeddings/babert-base-512/pytorch_models_babert_base_512_vocab_uncased_30522.txt"
config_path = "../embeddings/babert-base-512/pytorch_models_babert_base_512_bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = BertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
# Plug pretrained bert model
bert_model = BertForPreTraining.from_pretrained(
"../embeddings/babert-base-512/pytorch_models_babert_base_512_pytorch_model_babert_base_uncased_512.bin",
config=config)
model.bert = bert_model.bert
elif 'babert-bpe-mlm-large-uncased' == args['model_checkpoint']:
# babert_bpe
# Prepare config & tokenizer
vocab_path = "../embeddings/babert-bpe-mlm-large-uncased/pytorch_models_babert_uncased_large_vocab_uncased_30522.txt"
config_path = "../embeddings/babert-bpe-mlm-large-uncased/pytorch_models_babert_uncased_large_bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = BertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
# Plug pretrained bert model
bert_model = BertForPreTraining.from_pretrained(
"../embeddings/babert-bpe-mlm-large-uncased/pytorch_models_babert_uncased_large_pytorch_model_babert_large_778500.bin",
config=config)
model.bert = bert_model.bert
elif 'babert-bpe-mlm-uncased-128-dup10-5' == args['model_checkpoint']:
# babert_bpe_wwmlm
# Prepare config & tokenizer
vocab_path = "../embeddings/babert-bpe-mlm-uncased-128-dup10-5/vocab.txt"
config_path = "../embeddings/babert-bpe-mlm-uncased-128-dup10-5/bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = BertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
# Plug pretrained bert model
bert_model = BertForPreTraining.from_pretrained(
"../embeddings/babert-bpe-mlm-uncased-128-dup10-5/pytorch_model.bin",
config=config)
model.bert = bert_model.bert
elif 'bert-base-multilingual' in args['model_checkpoint']:
# bert-base-multilingual-uncased or bert-base-multilingual-cased
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = BertTokenizer.from_pretrained(args['model_checkpoint'])
config = BertConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'token_classification' == args['task']:
model = BertForWordClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'xlm-mlm' in args['model_checkpoint']:
# xlm-mlm-100-1280
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = XLMTokenizer.from_pretrained(args['model_checkpoint'])
config = XLMConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = XLMForSequenceClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'token_classification' == args['task']:
model = XLMForWordClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'multi_label_classification' == args['task']:
model = XLMForMultiLabelClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'xlm-roberta' in args['model_checkpoint']:
# xlm-roberta-base or xlm-roberta-large
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = XLMRobertaTokenizer.from_pretrained(
args['model_checkpoint'])
config = XLMRobertaConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = XLMRobertaForSequenceClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'token_classification' == args['task']:
model = XLMRobertaForWordClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'multi_label_classification' == args['task']:
model = XLMRobertaForMultiLabelClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'word2vec' in args['model_checkpoint'] or 'fasttext' in args[
'model_checkpoint']:
# Prepare config & tokenizer
vocab_path = args['vocab_path']
config_path = None
word_tokenizer = args['word_tokenizer_class']()
emb_path = args['embedding_path'][args['model_checkpoint']]
_, vocab_map = load_vocab(vocab_path)
tokenizer = SimpleTokenizer(vocab_map,
word_tokenizer,
lower=args["lower"])
vocab_list = list(tokenizer.vocab.keys())
config = BertConfig.from_pretrained('bert-base-uncased')
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
config.num_hidden_layers = args["num_layers"]
if args['model_checkpoint'] == 'word2vec-twitter':
embeddings = gen_embeddings(vocab_list, emb_path)
config.hidden_size = 400
config.num_attention_heads = 8
if args['model_checkpoint'] == 'fasttext-cc-id' or args[
'model_checkpoint'] == 'fasttext-cc-id-300-no-oov-uncased' or args[
'model_checkpoint'] == 'fasttext-4B-id-300-no-oov-uncased':
embeddings = gen_embeddings(vocab_list, emb_path, emb_dim=300)
config.hidden_size = 300
config.num_attention_heads = 10
config.vocab_size = len(embeddings)
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
model.bert.embeddings.word_embeddings.weight.data.copy_(
torch.FloatTensor(embeddings))
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
model.bert.embeddings.word_embeddings.weight.data.copy_(
torch.FloatTensor(embeddings))
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
model.bert.embeddings.word_embeddings.weight.data.copy_(
torch.FloatTensor(embeddings))
elif 'scratch' in args['model_checkpoint']:
vocab_path, config_path = None, None
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
config = BertConfig.from_pretrained("bert-base-uncased")
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
config.num_hidden_layers = args["num_layers"]
config.hidden_size = 300
config.num_attention_heads = 10
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config=config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config=config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config=config)
elif 'indobenchmark' in args['model_checkpoint']:
# indobenchmark models
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = BertTokenizer.from_pretrained(args['model_checkpoint'])
config = BertConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'token_classification' == args['task']:
model = BertForWordClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification.from_pretrained(
args['model_checkpoint'], config=config)
return model, tokenizer, vocab_path, config_path
train_loader = NerDataLoader(dataset=train_dataset,
max_seq_len=max_seq_len,
batch_size=batch_size,
num_workers=16,
shuffle=True)
valid_loader = NerDataLoader(dataset=valid_dataset,
max_seq_len=max_seq_len,
batch_size=eval_batch_size,
num_workers=16,
shuffle=False)
w2i, i2w = NerShopeeDataset.LABEL2INDEX, NerShopeeDataset.INDEX2LABEL
# Instantiate model
model = BertForWordClassification.from_pretrained(model_name,
config=config)
## RESUME
model_epoch = 2
output_model = "{}model-{}.pth".format(model_dir[:-1] + "-last_trained/",
model_epoch)
logger.info("Loaded model: {}".format(output_model))
checkpoint = torch.load(output_model, map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
learning_rate = 5e-6
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
model = model.cuda()
logger.info("Model: indobert-{}".format(model_version))