数据集：

• snli
• anli
• multi_nli
• multi_nli_mismatch
• fever 许可证：mit

这是一个强大的预训练RoBERTa-Large自然语言推理模型。

训练数据融合了多个知名NLI数据集：SNLI、MNLI、FEVER-NLI、ANLI (R1, R2, R3)。
还提供其他预训练NLI模型，包括RoBERTa、ALBert、BART、ELECTRA、XLNet。

由Yixin Nie训练，原始来源。

尝试以下代码片段：

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

if __name__ == '__main__':
    max_length = 256

    premise = "两名女子正相拥而立，手里拿着外卖餐盒。"
    hypothesis = "男人们正在熟食店外打架。"

    hg_model_hub_name = "ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli"
    # hg_model_hub_name = "ynie/albert-xxlarge-v2-snli_mnli_fever_anli_R1_R2_R3-nli"
    # hg_model_hub_name = "ynie/bart-large-snli_mnli_fever_anli_R1_R2_R3-nli"
    # hg_model_hub_name = "ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli"
    # hg_model_hub_name = "ynie/xlnet-large-cased-snli_mnli_fever_anli_R1_R2_R3-nli"

    tokenizer = AutoTokenizer.from_pretrained(hg_model_hub_name)
    model = AutoModelForSequenceClassification.from_pretrained(hg_model_hub_name)

    tokenized_input_seq_pair = tokenizer.encode_plus(premise, hypothesis,
                                                     max_length=max_length,
                                                     return_token_type_ids=True, truncation=True)

    input_ids = torch.Tensor(tokenized_input_seq_pair['input_ids']).long().unsqueeze(0)
    # 注意：bart模型没有'token_type_ids'，若使用bart请删除下面这行
    token_type_ids = torch.Tensor(tokenized_input_seq_pair['token_type_ids']).long().unsqueeze(0)
    attention_mask = torch.Tensor(tokenized_input_seq_pair['attention_mask']).long().unsqueeze(0)

    outputs = model(input_ids,
                    attention_mask=attention_mask,
                    token_type_ids=token_type_ids,
                    labels=None)
    # 注意：
    # "id2label": {
    #     "0": "蕴含",
    #     "1": "中立",
    #     "2": "矛盾"
    # },

    predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()  # 批处理大小仅为1

    print("前提：", premise)
    print("假设：", hypothesis)
    print("蕴含概率：", predicted_probability[0])
    print("中立概率：", predicted_probability[1])
    print("矛盾概率：", predicted_probability[2])

更多内容见此处。

引用：

@inproceedings{nie-etal-2020-adversarial,
    title = "对抗性自然语言推理：自然语言理解的新基准",
    author = "聂艺昕 和
      Williams, Adina 和
      Dinan, Emily 和
      Bansal, Mohit 和
      Weston, Jason 和
      Kiela, Douwe",
    booktitle = "第58届计算语言学协会年会论文集",
    year = "2020",
    publisher = "计算语言学协会",
}