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 and was introduced in the paper Sequence-to-Sequence Spanish Pre-trained Language Models.
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:
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)
@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}
}