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README.md

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 ---
 license: mit
+language: en
 ---
+
+# BART-SLED (SLiding-Encoder and Decoder, large-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 independently 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-large).
+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.
+
+This model was finetuned on the [GovReport](https://arxiv.org/abs/2104.02112)
+
+## 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 need to install `py-sled` in your environment (or clone the code from the [official repository](https://github.com/Mivg/SLED/blob/main/README.md))
+```
+pip install py-sled
+```
+For more installation instructions, see [here](https://github.com/Mivg/SLED#Installation).
+
+
+Once installed, SLED is fully compatible with HuggingFace's AutoClasses (AutoTokenizer, AutoConfig, AutoModel 
+and AutoModelForCausalLM) and can be loaded using the from_pretrained methods
+```python
+import sled   # *** required so that SledModels will be registered for the AutoClasses ***
+model = AutoModel.from_pretrained('tau/bart-large-sled')
+```
+
+Here is how to use this model in PyTorch:
+
+```python
+from sled import SledTokenizer, SledModel
+tokenizer = SledTokenizer.from_pretrained('tau/bart-large-sled')
+model = SledModel.from_pretrained('tau/bart-large-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 
+```python
+model = SledModelForConditionalGeneration.from_pretrained('tau/bart-large-sled')
+```
+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).
+```python
+import torch
+import sled   # *** required so that SledModels will be registered for the AutoClasses ***
+tokenizer = AutoTokenizer.from_pretrained('tau/bart-large-sled')
+model = AutoModel.from_pretrained('tau/bart-large-sled')
+document_input_ids = tokenizer("Dogs are great for you.", return_tensors="pt").input_ids
+prefix_input_ids = tokenizer("Are dogs good for you?", return_tensors="pt").input_ids
+input_ids = torch.cat((prefix_input_ids, document_input_ids), dim=-1)
+attention_mask = torch.ones_like(input_ids)
+prefix_length = torch.LongTensor([[prefix_input_ids.size(1)]])
+
+outputs = model(input_ids=input_ids, attention_mask=attention_mask, prefix_length=prefix_length)
+last_hidden_states = outputs.last_hidden_state
+```
+
+### 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 as well as GovReport by Huang 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}
+}
+```
+
+```bibtex
+@inproceedings{huang2021govreport,
+    title = "Efficient Attentions for Long Document Summarization",
+    author = "Huang, Luyang  and
+      Cao, Shuyang  and
+      Parulian, Nikolaus  and
+      Ji, Heng  and
+      Wang, Lu",
+    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
+    month = jun,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.naacl-main.112",
+    doi = "10.18653/v1/2021.naacl-main.112",
+    pages = "1419--1436"
+}
+```

config.json

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+{
+  "model_type": "tau/sled",
+  "underlying_config": "facebook/bart-large",
+  "context_size": 256,
+  "window_fraction": 0.5,
+  "prepend_prefix": true,
+  "encode_prefix": true,
+  "sliding_method": "dynamic"
+}

tokenizer_config.json

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+{
+  "tokenizer_class": "SledTokenizer",
+  "base_tokenizer": "facebook/bart-large",
+  "model_max_length": 16384
+}