metadata

license: mit
language:
  - es
metrics:
  - accuracy
pipeline_tag: fill-mask
widget:
  - text: Vamos a comer unos [MASK]
    example_title: Vamos a comer unos tacos
tags:
  - code
  - nlp
  - custom
  - bilma
tokenizer:
  - 'yes'

BILMA (Bert In Latin aMericA)

Bilma is a BERT implementation in tensorflow and trained on the Masked Language Model task under the https://sadit.github.io/regional-spanish-models-talk-2022/ datasets.

The accuracy of the models trained on the MLM task for different regions are:

Pre-requisites

You will need TensorFlow 2.4 or newer.

Quick guide

You can see the demo notebooks for a quick guide on how to use the models.

Clone this repository and then run

bash download-emoji15-bilma.sh

to download the MX model. Then to load the model you can use the code:

from bilma import bilma_model
vocab_file = "vocab_file_All.txt"
model_file = "bilma_small_MX_epoch-1_classification_epochs-13.h5"
model = bilma_model.load(model_file)
tokenizer = bilma_model.tokenizer(vocab_file=vocab_file,
max_length=280)

Now you will need some text:

texts = ["Tenemos tres dias sin internet ni senal de celular en el pueblo.",
         "Incomunicados en el siglo XXI tampoco hay servicio de telefonia fija",
         "Vamos a comer unos tacos",
         "Los del banco no dejan de llamarme"]
toks = tokenizer.tokenize(texts)

With this, you are ready to use the model

p = model.predict(toks)
tokenizer.decode_emo(p[1])

which produces the output: each emoji correspond to each entry in texts.