metadata
language: es
tags:
- Spanish
- BART
- biology
- medical
- seq2seq
license: mit
thumbnail: https://huggingface.co/Narrativa/NarbioBART/resolve/main/NarbioBART-logo.png
🦠 NarbioBART 🏥
NarbioBART (base) is a BART-like model trained on Spanish Biomedical Crawled Corpus.
BART is a transformer encoder-decoder (seq2seq) model 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.
This model is particularly effective when fine-tuned for text generation tasks (e.g., summarization, translation) but also works well for comprehension tasks (e.g., text classification, question answering).
Training details
- Dataset:
Spanish Biomedical Crawled Corpus- 90% for training / 10% for validation. - Training script: see here
Evaluation metrics 🧾
| Metric | # Value |
|---|---|
| Accuracy | 0.802 |
| Loss | 1.04 |
Benchmarks 🔨
WIP 🚧
How to use with transformers
from transformers import BartForConditionalGeneration, BartTokenizer
model_id = "Narrativa/NarbioBART"
model = BartForConditionalGeneration.from_pretrained(model_id, forced_bos_token_id=0)
tokenizer = BartTokenizer.from_pretrained(model_id)
def fill_mask_span(text):
batch = tokenizer(text, return_tensors="pt")
generated_ids = model.generate(batch["input_ids"])
print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True))
text = "your text with a <mask> token."
fill_mask_span(text)
Citation
@misc {narrativa_2023,
author = { {Narrativa} },
title = { NarbioBART (Revision c9a4e07) },
year = 2023,
url = { https://huggingface.co/Narrativa/NarbioBART },
doi = { 10.57967/hf/0500 },
publisher = { Hugging Face }
}