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--- |
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datasets: |
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- snli |
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- anli |
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- multi_nli |
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- multi_nli_mismatch |
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- fever |
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license: mit |
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--- |
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This is a strong pre-trained RoBERTa-Large NLI model. |
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The training data is a combination of well-known NLI datasets: [`SNLI`](https://nlp.stanford.edu/projects/snli/), [`MNLI`](https://cims.nyu.edu/~sbowman/multinli/), [`FEVER-NLI`](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [`ANLI (R1, R2, R3)`](https://github.com/facebookresearch/anli). |
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Other pre-trained NLI models including `RoBERTa`, `ALBert`, `BART`, `ELECTRA`, `XLNet` are also available. |
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Trained by [Yixin Nie](https://easonnie.github.io), [original source](https://github.com/facebookresearch/anli). |
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Try the code snippet below. |
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``` |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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if __name__ == '__main__': |
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max_length = 256 |
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premise = "Two women are embracing while holding to go packages." |
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hypothesis = "The men are fighting outside a deli." |
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hg_model_hub_name = "ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli" |
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# hg_model_hub_name = "ynie/albert-xxlarge-v2-snli_mnli_fever_anli_R1_R2_R3-nli" |
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# hg_model_hub_name = "ynie/bart-large-snli_mnli_fever_anli_R1_R2_R3-nli" |
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# hg_model_hub_name = "ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli" |
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# hg_model_hub_name = "ynie/xlnet-large-cased-snli_mnli_fever_anli_R1_R2_R3-nli" |
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tokenizer = AutoTokenizer.from_pretrained(hg_model_hub_name) |
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model = AutoModelForSequenceClassification.from_pretrained(hg_model_hub_name) |
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tokenized_input_seq_pair = tokenizer.encode_plus(premise, hypothesis, |
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max_length=max_length, |
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return_token_type_ids=True, truncation=True) |
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input_ids = torch.Tensor(tokenized_input_seq_pair['input_ids']).long().unsqueeze(0) |
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# remember bart doesn't have 'token_type_ids', remove the line below if you are using bart. |
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token_type_ids = torch.Tensor(tokenized_input_seq_pair['token_type_ids']).long().unsqueeze(0) |
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attention_mask = torch.Tensor(tokenized_input_seq_pair['attention_mask']).long().unsqueeze(0) |
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outputs = model(input_ids, |
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attention_mask=attention_mask, |
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token_type_ids=token_type_ids, |
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labels=None) |
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# Note: |
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# "id2label": { |
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# "0": "entailment", |
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# "1": "neutral", |
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# "2": "contradiction" |
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# }, |
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predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist() # batch_size only one |
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print("Premise:", premise) |
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print("Hypothesis:", hypothesis) |
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print("Entailment:", predicted_probability[0]) |
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print("Neutral:", predicted_probability[1]) |
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print("Contradiction:", predicted_probability[2]) |
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``` |
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More in [here](https://github.com/facebookresearch/anli/blob/master/src/hg_api/interactive_eval.py). |
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Citation: |
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``` |
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@inproceedings{nie-etal-2020-adversarial, |
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title = "Adversarial {NLI}: A New Benchmark for Natural Language Understanding", |
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author = "Nie, Yixin and |
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Williams, Adina and |
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Dinan, Emily and |
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Bansal, Mohit and |
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Weston, Jason and |
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Kiela, Douwe", |
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booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", |
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year = "2020", |
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publisher = "Association for Computational Linguistics", |
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} |
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``` |
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