transformers pytorch tau/sled en arxiv:2104.07091 arxiv:2208.00748 arxiv:1910.13461 license:mit endpoints_compatible region:us
---
language: en
license: mit
---

# 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 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-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.

This model was finetuned on the [SummScreenFD](https://arxiv.org/abs/2104.07091)

## 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-base-sled')
```

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 
```python
model = SledModelForConditionalGeneration.from_pretrained('tau/bart-base-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-base-sled')
model = AutoModel.from_pretrained('tau/bart-base-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 SummScreenFD by Chen 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{Chen2022SummScreenAD,
  title={SummScreen: A Dataset for Abstractive Screenplay Summarization},
  author={Mingda Chen and Zewei Chu and Sam Wiseman and Kevin Gimpel},
  booktitle={ACL},
  year={2022}
}
```