Update README.md

README.md

@@ -1,3 +1,61 @@
 ---
 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 (LEDS) (base-sized model) 
+
+LED model based on [BARTO](https://huggingface.co/vgaraujov/bart-base-spanish). It was introduced in the paper [Sequence-to-Sequence Spanish Pre-trained Language Models](https://arxiv.org/abs/2309.11259). 
+
+## Model description
+
+LEDS 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}
+}
+```