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🦠 NarbioBART πŸ₯

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 πŸ”¨

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 }
}
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