Migrate model card from transformers-repo

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Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/joeddav/xlm-roberta-large-xnli/README.md

README.md

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+---
+language: multilingual
+tags:
+- text-classification
+- pytorch
+- tensorflow
+datasets:
+- multi_nli
+- xnli
+license: mit
+pipeline_tag: zero-shot-classification
+widget:
+- text: "За кого вы голосуете в 2020 году?"
+  labels: "politique étrangère, Europe, élections, affaires, politique"
+- text: "لمن تصوت في 2020؟"
+  labels: "السياسة الخارجية, أوروبا, الانتخابات, الأعمال, السياسة"
+- text: "2020'de kime oy vereceksiniz?"
+  labels: "dış politika, Avrupa, seçimler, ticaret, siyaset"
+---
+
+# xlm-roberta-large-xnli
+
+## Model Description
+
+This model takes [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) and fine-tunes it on a combination of NLI data in 15 languages. It is intended to be used for zero-shot text classification, such as with the Hugging Face [ZeroShotClassificationPipeline](https://huggingface.co/transformers/master/main_classes/pipelines.html#transformers.ZeroShotClassificationPipeline).
+
+## Intended Usage
+
+This model is intended to be used for zero-shot text classification, especially in languages other than English. It is fine-tuned on XNLI, which is a multilingual NLI dataset. The model can therefore be used with any of the languages in the XNLI corpus:
+
+- English
+- French
+- Spanish
+- German
+- Greek
+- Bulgarian
+- Russian
+- Turkish
+- Arabic
+- Vietnamese
+- Thai
+- Chinese
+- Hindi
+- Swahili
+- Urdu
+
+Since the base model was pre-trained trained on 100 different languages, the
+model has shown some effectiveness in languages beyond those listed above as
+well. See the full list of pre-trained languages in appendix A of the
+[XLM Roberata paper](https://arxiv.org/abs/1911.02116)
+
+For English-only classification, it is recommended to use
+[bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) or
+[a distilled bart MNLI model](https://huggingface.co/models?filter=pipeline_tag%3Azero-shot-classification&search=valhalla).
+
+#### With the zero-shot classification pipeline
+
+The model can be loaded with the `zero-shot-classification` pipeline like so:
+
+```python
+from transformers import pipeline
+classifier = pipeline("zero-shot-classification",
+                      model="joeddav/xlm-roberta-large-xnli")
+```
+
+You can then classify in any of the above languages. You can even pass the labels in one language and the sequence to
+classify in another:
+
+```python
+# we will classify the Russian translation of, "Who are you voting for in 2020?"
+sequence_to_classify = "За кого вы голосуете в 2020 году?"
+# we can specify candidate labels in Russian or any other language above:
+candidate_labels = ["Europe", "public health", "politics"]
+classifier(sequence_to_classify, candidate_labels)
+# {'labels': ['politics', 'Europe', 'public health'],
+#  'scores': [0.9048484563827515, 0.05722189322113991, 0.03792969882488251],
+#  'sequence': 'За кого вы голосуете в 2020 году?'}
+```
+
+The default hypothesis template is the English, `This text is {}`. If you are working strictly within one language, it
+may be worthwhile to translate this to the language you are working with:
+
+```python
+sequence_to_classify = "¿A quién vas a votar en 2020?"
+candidate_labels = ["Europa", "salud pública", "política"]
+hypothesis_template = "Este ejemplo es {}."
+classifier(sequence_to_classify, candidate_labels, hypothesis_template=hypothesis_template)
+# {'labels': ['política', 'Europa', 'salud pública'],
+#  'scores': [0.9109585881233215, 0.05954807624220848, 0.029493311420083046],
+#  'sequence': '¿A quién vas a votar en 2020?'}
+```
+
+#### With manual PyTorch
+
+```python
+# pose sequence as a NLI premise and label as a hypothesis
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+nli_model = AutoModelForSequenceClassification.from_pretrained('joeddav/xlm-roberta-large-xnli')
+tokenizer = AutoTokenizer.from_pretrained('joeddav/xlm-roberta-large-xnli')
+
+premise = sequence
+hypothesis = f'This example is {label}.'
+
+# run through model pre-trained on MNLI
+x = tokenizer.encode(premise, hypothesis, return_tensors='pt',
+                     truncation_strategy='only_first')
+logits = nli_model(x.to(device))[0]
+
+# we throw away "neutral" (dim 1) and take the probability of
+# "entailment" (2) as the probability of the label being true 
+entail_contradiction_logits = logits[:,[0,2]]
+probs = entail_contradiction_logits.softmax(dim=1)
+prob_label_is_true = probs[:,1]
+```
+
+## Training
+
+This model was pre-trained on set of 100 languages, as described in
+[the original paper](https://arxiv.org/abs/1911.02116). It was then fine-tuned on the task of NLI on the concatenated
+MNLI train set and the XNLI validation and test sets. Finally, it was trained for one additional epoch on only XNLI
+data where the translations for the premise and hypothesis are shuffled such that the premise and hypothesis for
+each example come from the same original English example but the premise and hypothesis are of different languages.