Jaren/chinese-roberta-wwm-ext-large-NLI

This model used hfl/chinese-roberta-wwm-ext-large backbone and was trained on SNLI, MNLI, DNLI, KvPI, OCNLI, CMNLI data in Chinese version. Model structures are as follows:

class RobertaForSequenceClassification(nn.Module):
    def __init__(self, tagset_size):
        super(RobertaForSequenceClassification, self).__init__()
        self.tagset_size = tagset_size

        self.roberta_single= AutoModel.from_pretrained(pretrain_model_dir)
        self.single_hidden2tag = RobertaClassificationHead(bert_hidden_dim, tagset_size)

    def forward(self, input_ids, input_mask):
        outputs_single = self.roberta_single(input_ids, input_mask, None)
        hidden_states_single = outputs_single[1]#torch.tanh(self.hidden_layer_2(torch.tanh(self.hidden_layer_1(outputs_single[1])))) #(batch, hidden)

        score_single = self.single_hidden2tag(hidden_states_single) #(batch, tag_set)
        return score_single



class RobertaClassificationHead(nn.Module):
    def __init__(self, bert_hidden_dim, num_labels):
        super(RobertaClassificationHead, self).__init__()
        self.dense = nn.Linear(bert_hidden_dim, bert_hidden_dim)
        self.dropout = nn.Dropout(0.1)
        self.out_proj = nn.Linear(bert_hidden_dim, num_labels)

    def forward(self, features):
        x = features#[:, 0, :]  # take <s> token (equiv. to [CLS])
        x = self.dropout(x)
        x = self.dense(x)
        x = torch.tanh(x)
        x = self.dropout(x)
        x = self.out_proj(x)
        return x
model = RobertaForSequenceClassification(num_labels)
model.load_state_dict(torch.load(args.model_save_path+'Roberta_large_model.pt', map_location=device))