def __init__(self, word2ix, predicate_num, model_name="roberta"):
super(BertRelationExtrac, self).__init__()
self.predicate_num = predicate_num
config = ""
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig, BertPredictionHeadTransform, BertLayerNorm
config = BertConfig(len(word2ix))
self.bert = BertModel(config)
self.layer_norm = BertLayerNorm(config.hidden_size)
self.layer_norm_cond = BertLayerNorm(config.hidden_size,
conditional=True)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel, BertPredictionHeadTransform, BertLayerNorm
config = BertConfig(len(word2ix))
self.bert = BertModel(config)
self.layer_norm = BertLayerNorm(config.hidden_size)
self.layer_norm_cond = BertLayerNorm(config.hidden_size,
conditional=True)
else:
raise Exception("model_name_err")
self.subject_pred = nn.Linear(config.hidden_size, 2)
self.activation = nn.Sigmoid()
self.object_pred = nn.Linear(config.hidden_size,
2 * self.predicate_num)
def __init__(self, word2ix, model_name="roberta"):
super().__init__()
self.config = ""
self.word2ix = word2ix
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig, BertLayerNorm, BertPredictionHeadTransform, BertLMPredictionHead
self.config = BertConfig(len(self.word2ix))
self.bert = BertModel(self.config)
self.layer_norm = BertLayerNorm(self.config.hidden_size)
self.layer_norm_cond = BertLayerNorm(self.config.hidden_size,
conditional=True)
self.transform = BertPredictionHeadTransform(self.config)
self.decoder = BertLMPredictionHead(
self.config, self.bert.embeddings.word_embeddings.weight)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel, BertLayerNorm, BertPredictionHeadTransform, BertLMPredictionHead
self.config = BertConfig(len(self.word2ix))
self.bert = BertModel(self.config)
self.layer_norm = BertLayerNorm(self.config.hidden_size)
self.layer_norm_cond = BertLayerNorm(self.config.hidden_size,
conditional=True)
self.transform = BertPredictionHeadTransform(self.config)
self.decoder = BertLMPredictionHead(
self.config, self.bert.embeddings.word_embeddings.weight)
else:
raise Exception("model_name_err")
self.device = torch.device("cpu")
def __init__(self, word2ix, model_name="roberta", tokenizer=None):
super(Seq2SeqModel, self).__init__()
self.word2ix = word2ix
if tokenizer is None:
self.tokenizer = Tokenizer(word2ix)
else:
self.tokenizer = tokenizer
config = ""
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig, BertLMPredictionHead
config = BertConfig(len(word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(
config, self.bert.embeddings.word_embeddings.weight)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel, BertLMPredictionHead
config = BertConfig(len(word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(
config, self.bert.embeddings.word_embeddings.weight)
else:
raise Exception("model_name_err")
self.hidden_dim = config.hidden_size
self.vocab_size = len(word2ix)
def __init__(self, word2ix, target_size, model_name="roberta"):
super(BertClsClassifier, self).__init__()
self.word2ix = word2ix
self.target_size = target_size
config = ""
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
else:
raise Exception("model_name_err")
self.final_dense = nn.Linear(config.hidden_size, self.target_size)
def __init__(self, vocab_path, target_size, model_name="roberta"):
super(BertEncoder, self).__init__()
self.word2ix = load_chinese_base_vocab(vocab_path)
self.tokenizer = Tokenizer(self.word2ix)
self.target_size = target_size
config = ""
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
else:
raise Exception("model_name_err")
self.final_dense = nn.Linear(config.hidden_size, self.target_size)
def __init__(self, word2ix, target_size, model_name="roberta"):
super(BertSeqLabeling, self).__init__()
self.target_size = target_size
config = ""
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig, BertPredictionHeadTransform
config = BertConfig(len(word2ix))
self.bert = BertModel(config)
self.transform = BertPredictionHeadTransform(config)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel, BertPredictionHeadTransform
config = BertConfig(len(word2ix))
self.bert = BertModel(config)
self.transform = BertPredictionHeadTransform(config)
else:
raise Exception("model_name_err")
self.final_dense = nn.Linear(config.hidden_size, self.target_size)
def __init__(self, vocab_path, model_name="roberta"):
super(Seq2SeqModel, self).__init__()
self.word2ix = load_chinese_base_vocab(vocab_path)
self.tokenizer = Tokenizer(self.word2ix)
config = ""
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig, BertLMPredictionHead
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(config, self.bert.embeddings.word_embeddings.weight)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel, BertLMPredictionHead
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(config, self.bert.embeddings.word_embeddings.weight)
else :
raise Exception("model_name_err")
self.hidden_dim = config.hidden_size
self.vocab_size = config.vocab_size
def __init__(self, vocab_path, target_size, model_name="roberta"):
super(BertSeqLabelingCRF, self).__init__()
self.word2ix = load_chinese_base_vocab(vocab_path)
self.target_size = target_size
config = ""
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig, BertPredictionHeadTransform
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
self.transform = BertPredictionHeadTransform(config)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel, BertPredictionHeadTransform
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
self.transform = BertPredictionHeadTransform(config)
else:
raise Exception("model_name_err")
self.final_dense = nn.Linear(config.hidden_size, self.target_size)
self.crf_layer = CRFLayer(self.target_size)
def __init__(self, word2ix, model_name="roberta"):
super().__init__()
self.config = ""
self.word2ix = word2ix
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig, BertLayerNorm
self.config = BertConfig(len(self.word2ix))
self.bert = BertModel(self.config)
self.layer_norm = BertLayerNorm(self.config.hidden_size)
self.layer_norm_cond = BertLayerNorm(self.config.hidden_size,
conditional=True)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel, BertLayerNorm
self.config = BertConfig(len(self.word2ix))
self.bert = BertModel(self.config)
self.layer_norm = BertLayerNorm(self.config.hidden_size)
self.layer_norm_cond = BertLayerNorm(self.config.hidden_size,
conditional=True)
else:
raise Exception("model_name_err")
self.device = torch.device("cpu")
