This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.2778 Precision: 0.5603 Recall: 0.3142 F1: 0.4026 Accuracy: 0.9416 Model description
--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: my_wnut_model results: [] --- # my_wnut_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2778 - Precision: 0.5603 - Recall: 0.3142 - F1: 0.4026 - Accuracy: 0.9416 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2812 | 0.4948 | 0.2660 | 0.3460 | 0.9386 | | No log | 2.0 | 426 | 0.2778 | 0.5603 | 0.3142 | 0.4026 | 0.9416 | ### Framework versions - Transformers 5.10.1 - Pytorch 2.11.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.2778 Precision: 0.5603 Recall: 0.3142 F1: 0.4026 Accuracy: 0.9416 Model description
