--- license: apache-2.0 base_model: distilbert-base-multilingual-cased tags: - generated_from_trainer - pop science model-index: - name: results results: [] language: - es metrics: - f1 - roc_auc pipeline_tag: text-classification datasets: - teoremaclon/popscitweetsbyarea library_name: transformers --- # results This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1582 - Roc Auc: 0.7868 - Hamming Loss: 0.0625 - F1 Score: 0.6231 ## Model description Label interpretation: 'astronomía y espacio' (astronomy and space): 0, 'matemáticas' (mathematics): 1, 'física' (physics): 2, 'biología' (biology): 3, 'medicina y salud' (health and medicine): 4, 'tecnología' (technology): 5, 'química' (chemistry): 6, 'historia de la ciencia' (history of science): 7, 'ingeniería' (engineering): 8, 'computación' (computation): 9, 'ciencias de la tierra' (earth science): 10, 'materia y energia' (matter and energy): 11, 'psicología' (psychology): 12, 'invitación a evento o a recursos' (invitation to event or resources): 13, 'efeméride' (anniversary/day of): 14, 'mujeres en la ciencia' (women in science): 15, 'cultura pop' (pop culture): 16, 'otro' (other): 17 ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Tokenizers 0.15.2