bert-finetuned-sem_eval-english
This model is a fine-tuned version of bert-base-uncased on the sem_eval_2018_task_1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3283
- F1: 0.6578
- Roc Auc: 0.7603
- Accuracy: 0.2483
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: 8
- eval_batch_size: 8
- 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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.408 | 1.0 | 855 | 0.3283 | 0.6578 | 0.7603 | 0.2483 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 2.15.0
- Tokenizers 0.20.3
- Downloads last month
- 104
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 rbc33/bert-finetuned-sem_eval-english
Base model
google-bert/bert-base-uncasedDataset used to train rbc33/bert-finetuned-sem_eval-english
Evaluation results
- F1 on sem_eval_2018_task_1validation set self-reported0.658
- Accuracy on sem_eval_2018_task_1validation set self-reported0.248