Create model card for pre-trained NLI models. (#7864)

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* Update model_cards/ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

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Co-authored-by: Julien Chaumond <chaumond@gmail.com>

model_cards/ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli/README.md

+---
+datasets:
+- snli
+- anli
+- multi_nli
+- multi_nli_mismatch
+- fever
+license: mit
+---
+This is a strong pre-trained RoBERTa-Large NLI model.  
+
+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).  
+Other pre-trained NLI models including `RoBERTa`, `ALBert`, `BART`, `ELECTRA`, `XLNet` are also available.  
+
+Trained by [Yixin Nie](https://easonnie.github.io), [original source](https://github.com/facebookresearch/anli).
+
+Try the code snippet below.
+```
+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])
+```
+
+More in [here](https://github.com/facebookresearch/anli/blob/master/src/hg_api/interactive_eval.py).
+
+Citation:
+```
+@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",
+}
+```