---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: db_himp_4.2
  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. -->

# db_himp_4.2

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6350
- Accuracy: 0.9290
- F1 Weighted: 0.9284
- F1 Macro: 0.9315
- Precision Weighted: 0.9289
- Recall Weighted: 0.9290
- Precision Macro: 0.9297
- Recall Macro: 0.9339

## 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: 2.5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 9
- label_smoothing_factor: 0.05

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Weighted | F1 Macro | Precision Weighted | Recall Weighted | Precision Macro | Recall Macro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:------------------:|:---------------:|:---------------:|:------------:|
| 1.3724        | 1.0   | 772  | 1.0129          | 0.8106   | 0.8086      | 0.8082   | 0.8128             | 0.8106          | 0.8117          | 0.8114       |
| 0.8057        | 2.0   | 1544 | 0.7635          | 0.8810   | 0.8796      | 0.8828   | 0.8818             | 0.8810          | 0.8815          | 0.8872       |
| 0.6641        | 3.0   | 2316 | 0.7049          | 0.9038   | 0.9027      | 0.9052   | 0.9038             | 0.9038          | 0.9043          | 0.9081       |
| 0.5781        | 4.0   | 3088 | 0.6703          | 0.9156   | 0.9152      | 0.9181   | 0.9163             | 0.9156          | 0.9181          | 0.9195       |
| 0.5372        | 5.0   | 3860 | 0.6480          | 0.9226   | 0.9219      | 0.9248   | 0.9225             | 0.9226          | 0.9229          | 0.9277       |
| 0.5081        | 6.0   | 4632 | 0.6425          | 0.9253   | 0.9248      | 0.9275   | 0.9256             | 0.9253          | 0.9259          | 0.9300       |
| 0.4850        | 7.0   | 5404 | 0.6362          | 0.9280   | 0.9276      | 0.9305   | 0.9282             | 0.9280          | 0.9297          | 0.9320       |
| 0.4638        | 8.0   | 6176 | 0.6352          | 0.9288   | 0.9283      | 0.9312   | 0.9288             | 0.9288          | 0.9298          | 0.9334       |
| 0.4603        | 9.0   | 6948 | 0.6350          | 0.9290   | 0.9284      | 0.9315   | 0.9289             | 0.9290          | 0.9297          | 0.9339       |


### Framework versions

- Transformers 5.12.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2