token-classification
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2569
- Precision: 0.5474
- Recall: 0.3744
- F1: 0.4447
- Accuracy: 0.9452
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1128 | 1.0 | 213 | 0.2483 | 0.5240 | 0.3744 | 0.4368 | 0.9445 |
| 0.0775 | 2.0 | 426 | 0.2569 | 0.5474 | 0.3744 | 0.4447 | 0.9452 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
- 11
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for Park-Hip-02/token-classification
Base model
distilbert/distilbert-base-uncasedDataset used to train Park-Hip-02/token-classification
Evaluation results
- Precision on wnut_17test set self-reported0.547
- Recall on wnut_17test set self-reported0.374
- F1 on wnut_17test set self-reported0.445
- Accuracy on wnut_17test set self-reported0.945