library_name: transformers | |
license: apache-2.0 | |
base_model: distilbert-base-uncased | |
tags: | |
- generated_from_trainer | |
metrics: | |
- accuracy | |
- precision | |
- recall | |
- f1 | |
model-index: | |
- name: results | |
results: [] | |
<!-- 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. --> | |
# results | |
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. | |
It achieves the following results on the evaluation set: | |
- Loss: 0.2696 | |
- Accuracy: 0.92 | |
- Precision: 0.9170 | |
- Recall: 0.9236 | |
- F1: 0.9203 | |
## 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: 5e-05 | |
- train_batch_size: 16 | |
- eval_batch_size: 16 | |
- seed: 42 | |
- optimizer: Use 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: 2 | |
### Training results | |
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | | |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | |
| 0.2796 | 1.0 | 2500 | 0.2519 | 0.907 | 0.9299 | 0.8804 | 0.9045 | | |
| 0.1406 | 2.0 | 5000 | 0.2696 | 0.92 | 0.9170 | 0.9236 | 0.9203 | | |
### Framework versions | |
- Transformers 4.47.0 | |
- Pytorch 2.5.1+cu121 | |
- Datasets 3.2.0 | |
- Tokenizers 0.21.0 | |