arrow-hf
/

bart-large

@@ -1,62 +1,25 @@
 ---
 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-decoder (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 PyTorch:
 ```python
-from transformers import BartTokenizer, BartModel
-tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
-model = BartModel.from_pretrained('facebook/bart-large')
-inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-outputs = model(**inputs)
-last_hidden_states = outputs.last_hidden_state
 ```
-### 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}
-}
-```

 ---
+tags:
+  - bart
+  - mirror
+library_name: transformers
 license: apache-2.0
 ---
+# BART-large (full mirror)
+Full mirror of [facebook/bart-large](https://huggingface.co/facebook/bart-large) — includes model weights (`pytorch_model.bin`, ~971 MB), tokenizer files, config.
+Mirrored via `huggingface_hub.snapshot_download` for archival; identical to the upstream snapshot at mirror time.
+## Usage
 ```python
+from transformers import AutoModel, AutoTokenizer
+model = AutoModel.from_pretrained("arrow-hf/bart-large")
+tokenizer = AutoTokenizer.from_pretrained("arrow-hf/bart-large")
 ```
+## Related
+The tokenizer is used by [arrow-hf/xvla-robotwin-stack-bowls-two-40pct](https://huggingface.co/arrow-hf/xvla-robotwin-stack-bowls-two-40pct) (max_length=50). The X-VLA model uses the BART tokenizer vocabulary; the Florence2 vision-language backbone has its own learned weights and does not load BART's weights directly.