Create README.md

README.md

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+---
+license: apache-2.0
+language: en
+---
+
+# BART (large-sized model) 
+
+BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/bart). 
+
+Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
+
+BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).
+
+## Intended uses & limitations
+
+You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=bart) to look for fine-tuned versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model in tf_transformers:
+
+```python
+from tf_transformers.models import BartModel
+from transformers import BartTokenizer
+
+tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
+model = BartModel.from_pretrained('facebook/bart-large')
+
+inputs_tf = {}
+inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+
+inputs_tf["encoder_input_ids"] = inputs["input_ids"]
+inputs_tf["encoder_input_mask"] = inputs["attention_mask"]
+inputs_tf["decoder_input_ids"] = decoder_input_ids
+outputs_tf = model(inputs_tf)
+
+```
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-1910-13461,
+  author    = {Mike Lewis and
+               Yinhan Liu and
+               Naman Goyal and
+               Marjan Ghazvininejad and
+               Abdelrahman Mohamed and
+               Omer Levy and
+               Veselin Stoyanov and
+               Luke Zettlemoyer},
+  title     = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language
+               Generation, Translation, and Comprehension},
+  journal   = {CoRR},
+  volume    = {abs/1910.13461},
+  year      = {2019},
+  url       = {http://arxiv.org/abs/1910.13461},
+  eprinttype = {arXiv},
+  eprint    = {1910.13461},
+  timestamp = {Thu, 31 Oct 2019 14:02:26 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```