Roberta Large Snli Mnli Fever Anli R1 R2 R3 Nli

🚀 RoBERTa-Large NLI Pre-trained Model

This is a powerful pre-trained RoBERTa-Large NLI model. It combines multiple well - known NLI datasets for training, offering high - quality performance in natural language inference tasks. Other pre - trained NLI models such as RoBERTa, ALBert, BART, ELECTRA, and XLNet are also available.

🚀 Quick Start

Prerequisites

The training data of this model is a combination of the following well - known NLI datasets:

• SNLI
• MNLI
• FEVER - NLI
• ANLI (R1, R2, R3)

Code Example

You can try the following code snippet to use this model:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

if __name__ == '__main__':
    max_length = 256

    premise = "Two women are embracing while holding to go packages."
    hypothesis = "The men are fighting outside a deli."

    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)
    # remember bart doesn't have 'token_type_ids', remove the line below if you are using bart.
    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)
    # Note:
    # "id2label": {
    #     "0": "entailment",
    #     "1": "neutral",
    #     "2": "contradiction"
    # },

    predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()  # batch_size only one

    print("Premise:", premise)
    print("Hypothesis:", hypothesis)
    print("Entailment:", predicted_probability[0])
    print("Neutral:", predicted_probability[1])
    print("Contradiction:", predicted_probability[2])

For more information, please refer to here.

📚 Documentation

Citation

If you use this model in your research, please cite the following paper:

@inproceedings{nie-etal-2020-adversarial,
    title = "Adversarial {NLI}: A New Benchmark for Natural Language Understanding",
    author = "Nie, Yixin  and
      Williams, Adina  and
      Dinan, Emily  and
      Bansal, Mohit  and
      Weston, Jason  and
      Kiela, Douwe",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    year = "2020",
    publisher = "Association for Computational Linguistics",
}

📄 License

This project is licensed under the MIT license.

🔗 Acknowledgments

This model is trained by Yixin Nie, and the original source is provided by relevant research teams.