updated model card

README.md

@@ -2,3 +2,75 @@
 license: mit
 language: en
 ---
+
+# BART-SLED (SLiding-Encoder and Decoder, base-sized model) 
+
+SLED models use pretrained, short-range encoder-decoder models, and apply them over 
+long-text inputs by splitting the input into multiple overlapping chunks, encoding each independtly and perform fusion-in-decoder
+
+## Model description
+
+This SLED model is based on the BART model, which is described in its [model card](https://huggingface.co/facebook/bart-base).
+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). When used as a BART-SLED model, it can be applied on long text tasks.
+
+## 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. 
+
+### How to use
+To use the model, you first have to get a local copy of the SLED model from the [official repository](https://github.com/Mivg/SLED/blob/main/README.md).
+
+Here is how to use this model in PyTorch:
+
+```python
+from sled import SledTokenizer, SledModel
+tokenizer = SledTokenizer.from_pretrained('tau/bart-base-sled')
+model = SledModel.from_pretrained('tau/bart-base-sled')
+inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+outputs = model(**inputs)
+last_hidden_states = outputs.last_hidden_state
+```
+You can also replace SledModel by SledModelForConditionalGeneration for Seq2Seq generation 
+
+In case you wish to apply SLED on a task containing a prefix (e.g. question) which should be given as a context to 
+every chunk, you can pass the `prefix_length` tensor input as well (A LongTensor in the length of the batch size).
+
+Sled is fully compatible with the AutoClasses (AutoTokenizer, AutoConfig, AutoModel 
+and AutoModelForCausalLM) and can be loaded using the from_pretrained methods
+
+### BibTeX entry and citation info
+
+Please cite both the SLED [paper](https://arxiv.org/abs/2208.00748.pdf) and the BART [paper](https://arxiv.org/abs/1910.13461) by Lewis et al
+
+```bibtex
+@inproceedings{Ivgi2022EfficientLU,
+  title={Efficient Long-Text Understanding with Short-Text Models},
+  author={Maor Ivgi and Uri Shaham and Jonathan Berant},
+  year={2022}
+}
+```
+
+```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}
+}
+```