--- license: apache-2.0 language: "es" tags: - generated_from_trainer - sentiment - emotion widget: - text: "La vida no merece la pena" example_title: "Ejemplo 1" - text: "Para vivir así lo mejor es estar muerto" example_title: "Ejemplo 2" - text: "me siento triste por no poder viajar" example_title: "Ejemplo 3" - text: "Quiero terminar con todo" example_title: "Ejemplo 4" - text: "Disfruto de la vista" example_title: "Ejemplo 5" metrics: - accuracy model-index: - name: electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas results: [] --- # clasificacion-texto-suicida-finetuned-amazon-review This model is a fine-tuned version of [mrm8488/electricidad-small-discriminator](https://huggingface.co/mrm8488/electricidad-small-discriminator) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0458 - Accuracy: 0.9916 ## Model description More information needed ## 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: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Validation Loss | Accuracy | |:-------------:|:-----:|:---------------:|:--------:| | 0.161100 | 1.0 | 0.133057 | 0.952718 | | 0.134500 | 2.0 | 0.110966 | 0.960804 | | 0.108500 | 3.0 | 0.086417 | 0.970835 | | 0.099400 | 4.0 | 0.073618 | 0.974856 | | 0.090500 | 5.0 | 0.065231 | 0.979629 | | 0.080700 | 6.0 | 0.060849 | 0.982324 | | 0.069200 | 7.0 | 0.054718 | 0.986125 | | 0.060400 | 8.0 | 0.051153 | 0.985948 | | 0.048200 | 9.0 | 0.045747 | 0.989748 | | 0.045500 | 10.0 | 0.049992 | 0.988069 | | 0.043400 | 11.0 | 0.046325 | 0.990234 | | 0.034300 | 12.0 | 0.050746 | 0.989792 | | 0.032900 | 13.0 | 0.043434 | 0.991737 | | 0.028400 | 14.0 | 0.045003 | 0.991869 | | 0.022300 | 15.0 | 0.045819 | 0.991648 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6 ---