def __init__(self, config):
from model_utils import ContextPooler
super().__init__(config)
self.deberta = DebertaModel(config)
self.pooler = ContextPooler(config)
self.init_weights()
def get_transformers_model(
settings: Dict[str, Any],
model_name: str,
pretrained: bool = True,
ckptdir: Optional[Path] = None,
) -> PreTrainedModel:
model_path = model_name if pretrained else str(ckptdir)
config = AutoConfig.from_pretrained(model_path)
config.attention_probs_dropout_prob = settings.get(
'encoder_attn_dropout_rate', 0.1)
config.hidden_dropout_prob = settings.get('encoder_ffn_dropout_rate', 0.1)
config.layer_norm_eps = settings.get('layer_norm_eps', 1e-5)
if pretrained:
model = AutoModel.from_pretrained(model_name, config=config)
return model
# if you want not parameters but only model structure, each model class is needed.
if 'xlm' in model_name:
model = XLMModel(config=config)
elif 'albert' in model_name:
model = AlbertModel(config=config)
elif 'roberta' in model_name:
model = RobertaModel(config=config)
elif 'deberta-v2' in model_name:
model = DebertaV2Model(config=config)
elif 'deberta' in model_name:
model = DebertaModel(config=config)
elif 'bert' in model_name:
model = BertModel(config=config)
elif 'electra' in model_name:
model = ElectraModel(config=config)
else:
model = BertModel(config=config)
return model
def base_deberta():
from transformers import DebertaTokenizer, DebertaModel
tokenizer = DebertaTokenizer.from_pretrained('microsoft/deberta-base')
model = DebertaModel.from_pretrained('microsoft/deberta-base')
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
#输出最后一层的隐藏层状态
print(last_hidden_states)
def test_inference_no_head(self):
model = DebertaModel.from_pretrained("microsoft/deberta-base")
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
output = model(input_ids, attention_mask=attention_mask)[0]
# compare the actual values for a slice.
expected_slice = torch.tensor(
[[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]
)
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4), f"{output[:, 1:4, 1:4]}")
def test_inference_no_head(self):
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
model = DebertaModel.from_pretrained("microsoft/deberta-base")
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids)[0]
# compare the actual values for a slice.
expected_slice = torch.tensor(
[[[-0.0218, -0.6641, -0.3665], [-0.3907, -0.4716, -0.6640], [0.7461, 1.2570, -0.9063]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4), f"{output[:, :3, :3]}")
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.deberta = DebertaModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.loss_fct = nn.CrossEntropyLoss()
self.use_crf = config.use_crf
if self.use_crf:
self.crf_layer = Transformer_CRF(
num_labels=config.num_labels,
start_label_id=config.label2idx['CLS'])
else:
self.crf_layer = None
self.init_weights()
def create_and_check_deberta_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = DebertaModel(config=config)
model.to(torch_device)
model.eval()
sequence_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)[0]
sequence_output = model(input_ids, token_type_ids=token_type_ids)[0]
sequence_output = model(input_ids)[0]
self.parent.assertListEqual(list(sequence_output.size()), [self.batch_size, self.seq_length, self.hidden_size])
def test_model_from_pretrained(self):
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DebertaModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def __init__(self, config: DebertaConfig, model_path: str):
super(DeBertForOppo, self).__init__(config)
self.bert = DebertaModel.from_pretrained(model_path, config=config)
self.classifier = nn.Linear(2 * config.hidden_size, config.num_labels)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
