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 }
}

Narrativa/NarbioBART

🦠 NarbioBART 🏥

Training details

Evaluation metrics 🧾

Benchmarks 🔨

How to use with transformers

Citation

Narrativa/
NarbioBART

How to use with `transformers`