Instructions to use jorgeortizfuentes/tulio-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jorgeortizfuentes/tulio-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jorgeortizfuentes/tulio-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jorgeortizfuentes/tulio-bert") model = AutoModelForMaskedLM.from_pretrained("jorgeortizfuentes/tulio-bert") - Notebooks
- Google Colab
- Kaggle
Tulio
Tulio is a BERT model trained with Chilean Spanish. This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on Spanish Books and Small Chilean Spanish Corpus.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 20
- eval_batch_size: 20
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 20
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
Acknowledgments
We are grateful for the servers provided by the Computer Science Department of the University of Chile and the ReLeLa (Representations for Learning and Language) study group for the training of the model.
License Disclaimer
The license gpl-3.0 best describes our intentions for our work. However we are not sure that all the datasets used to train the model have licenses compatible with gpl-3.0. Please use at your own discretion and verify that the licenses of the original text resources match your needs.
Limitations
The training dataset was not censored in any way. Therefore, the model may contain unwanted ideological representations. Use with caution.
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