README.md

---
license: apache-2.0
language:
- es
datasets:
- large_spanish_corpus
- bertin-project/mc4-es-sampled
- oscar-corpus/OSCAR-2109
base_model: vgaraujov/bart-base-spanish
tags:
- text-generation-inference
widget:
- text: Quito es la capital de <mask>
---

# Longformer Encoder-Decoder Spanish (LEDO) (base-sized model) 

LEDO is based on [BARTO](https://huggingface.co/vgaraujov/bart-base-spanish) and was introduced in the paper [Sequence-to-Sequence Spanish Pre-trained Language Models](https://arxiv.org/abs/2309.11259). 

## Model description

LEDO is a BART-based model (transformer encoder-decoder) 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.

To process 16K tokens, the BARTO's position embedding matrix was simply copied 16 times.

BARTO is particularly effective when fine-tuned for long-range summarization and question answering.

## Intended uses & limitations

You can use the raw model for text infilling. However, the model is mainly meant to be fine-tuned on a supervised dataset.

This model does not have a slow tokenizer (LEDTokenizer).

### How to use

Here is how to use this model in PyTorch:

```python
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained('vgaraujov/led-base-16384-spanish')
model = AutoModel.from_pretrained('vgaraujov/led-base-16384-spanish')

inputs = tokenizer("Hola amigo, bienvenido a casa.", return_tensors="pt")
outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state
```

### Citation (BibTeX)

```bibtex
@misc{araujo2023sequencetosequence,
      title={Sequence-to-Sequence Spanish Pre-trained Language Models}, 
      author={Vladimir Araujo and Maria Mihaela Trusca and Rodrigo Tufiño and Marie-Francine Moens},
      year={2023},
      eprint={2309.11259},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```