metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: Training
results: []
Training
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1474
- Precision: 0.9421
- Recall: 0.8978
- F1: 0.9194
- Roc Auc: 0.9859
- Krippendorff Alpha: 0.8754
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: 6.7e-06
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Roc Auc | Krippendorff Alpha |
|---|---|---|---|---|---|---|---|---|
| 0.3425 | 1.0 | 247 | 0.3340 | 0.8489 | 0.7859 | 0.8162 | 0.9439 | 0.7187 |
| 0.2554 | 2.0 | 494 | 0.2263 | 0.8225 | 0.9183 | 0.8678 | 0.9651 | 0.7865 |
| 0.2351 | 3.0 | 741 | 0.1885 | 0.9087 | 0.8789 | 0.8936 | 0.9765 | 0.8352 |
| 0.1724 | 4.0 | 988 | 0.1892 | 0.9124 | 0.8798 | 0.8958 | 0.9773 | 0.8388 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1