| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: distilbert/distilbert-base-uncased |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - wnut_17 |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: distilbert_wnut_model |
| | results: |
| | - task: |
| | name: Token Classification |
| | type: token-classification |
| | dataset: |
| | name: wnut_17 |
| | type: wnut_17 |
| | config: wnut_17 |
| | split: test |
| | args: wnut_17 |
| | metrics: |
| | - name: Precision |
| | type: precision |
| | value: 0.5218476903870163 |
| | - name: Recall |
| | type: recall |
| | value: 0.3873957367933272 |
| | - name: F1 |
| | type: f1 |
| | value: 0.4446808510638298 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.946346885554273 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # distilbert_wnut_model |
| |
|
| | This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the wnut_17 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.3052 |
| | - Precision: 0.5218 |
| | - Recall: 0.3874 |
| | - F1: 0.4447 |
| | - Accuracy: 0.9463 |
| | |
| | ## 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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - num_epochs: 6 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | No log | 1.0 | 213 | 0.2801 | 0.5586 | 0.2428 | 0.3385 | 0.9384 | |
| | | No log | 2.0 | 426 | 0.2573 | 0.5228 | 0.2975 | 0.3792 | 0.9425 | |
| | | 0.1769 | 3.0 | 639 | 0.2859 | 0.5510 | 0.3253 | 0.4091 | 0.9450 | |
| | | 0.1769 | 4.0 | 852 | 0.2965 | 0.5499 | 0.3522 | 0.4294 | 0.9462 | |
| | | 0.0496 | 5.0 | 1065 | 0.2951 | 0.5123 | 0.3846 | 0.4394 | 0.9458 | |
| | | 0.0496 | 6.0 | 1278 | 0.3052 | 0.5218 | 0.3874 | 0.4447 | 0.9463 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.49.0 |
| | - Pytorch 2.6.0+cu124 |
| | - Datasets 3.4.1 |
| | - Tokenizers 0.21.1 |
| | |
