This model is a fine-tuned version of
NazaGara/NER-fine-tuned-BETO on the conll2002 dataset.
It achieves the following results on the evaluation set:
The model described here is a fine-tuned version of the BETO (BERT-based Spanish language model) for Named Entity Recognition (NER) tasks,
trained on the CoNLL-2002 dataset. BETO is a pre-trained language model specifically designed for the Spanish language, based on the BERT architecture.
By fine-tuning BETO on the CoNLL-2002 dataset, the model has been adapted to recognize and classify named entities such as persons, organizations,
locations, and other miscellaneous entities within Spanish text. The fine-tuning process involves adjusting the pre-trained model weights to better
fit the specific task of NER, thereby improving its performance and accuracy on Spanish text.
The training was performed using a GPU with 22.5 GB of RAM, 53 GB of system RAM, and 200 GB of disk space.
This setup ensured efficient handling of the large dataset and the computational demands of fine-tuning the model.
