def load_model(vocab): model = BertSCLSTM(3*len(vocab["chartoken2idx"]),vocab["token2idx"][ vocab["pad_token"] ],len(vocab["token_freq"]),early_concat=False) print(model) print( get_model_nparams(model) ) return model
def load_model(vocab): model = ElmoSCTransformer(3 * len(vocab["chartoken2idx"]), vocab["token2idx"][vocab["pad_token"]], len(vocab["token_freq"])) print(model) print(get_model_nparams(model)) return model
def load_model(vocab): model = SubwordBert(3 * len(vocab["chartoken2idx"]), vocab["token2idx"][vocab["pad_token"]], len(vocab["token_freq"])) print(model) print(get_model_nparams(model)) return model
def load_model(vocab): CHAR_EMBS_DIM = 100 model = CharLSTMWordLSTMModel( len(vocab["chartoken2idx"]), CHAR_EMBS_DIM, vocab["chartoken2idx"][vocab["char_pad_token"]], vocab["token2idx"][vocab["pad_token"]], len(vocab["token_freq"])) print(model) print(get_model_nparams(model)) return model