---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: output
  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. -->

# output

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1131
- F1: 0.9809

## 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: 4e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | F1     |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.3479        | 0.0856 | 50   | 0.1687          | 0.9652 |
| 0.1711        | 0.1712 | 100  | 0.2640          | 0.9259 |
| 0.1464        | 0.2568 | 150  | 0.1211          | 0.9762 |
| 0.099         | 0.3425 | 200  | 0.0968          | 0.9803 |
| 0.0991        | 0.4281 | 250  | 0.1052          | 0.9768 |
| 0.1037        | 0.5137 | 300  | 0.0974          | 0.9759 |
| 0.096         | 0.5993 | 350  | 0.0838          | 0.9812 |
| 0.0773        | 0.6849 | 400  | 0.1089          | 0.9765 |
| 0.0824        | 0.7705 | 450  | 0.1058          | 0.9809 |
| 0.1142        | 0.8562 | 500  | 0.0911          | 0.9794 |
| 0.0984        | 0.9418 | 550  | 0.0816          | 0.9803 |
| 0.0821        | 1.0274 | 600  | 0.0891          | 0.9835 |
| 0.0761        | 1.1130 | 650  | 0.0953          | 0.9791 |
| 0.0706        | 1.1986 | 700  | 0.1002          | 0.9835 |
| 0.0745        | 1.2842 | 750  | 0.0872          | 0.9827 |
| 0.0831        | 1.3699 | 800  | 0.1102          | 0.9794 |
| 0.0828        | 1.4555 | 850  | 0.0895          | 0.9833 |
| 0.0656        | 1.5411 | 900  | 0.0969          | 0.9812 |
| 0.0859        | 1.6267 | 950  | 0.0856          | 0.9841 |
| 0.0608        | 1.7123 | 1000 | 0.0873          | 0.9797 |
| 0.0751        | 1.7979 | 1050 | 0.0818          | 0.9835 |
| 0.0561        | 1.8836 | 1100 | 0.0808          | 0.9833 |
| 0.0753        | 1.9692 | 1150 | 0.0839          | 0.9838 |
| 0.0758        | 2.0548 | 1200 | 0.0955          | 0.9824 |
| 0.0413        | 2.1404 | 1250 | 0.1082          | 0.9827 |
| 0.0572        | 2.2260 | 1300 | 0.0906          | 0.9821 |
| 0.0597        | 2.3116 | 1350 | 0.0847          | 0.9830 |
| 0.0506        | 2.3973 | 1400 | 0.0870          | 0.9838 |
| 0.0629        | 2.4829 | 1450 | 0.0843          | 0.9815 |
| 0.0404        | 2.5685 | 1500 | 0.1024          | 0.9830 |
| 0.0476        | 2.6541 | 1550 | 0.0809          | 0.9833 |
| 0.0556        | 2.7397 | 1600 | 0.0895          | 0.9830 |
| 0.0803        | 2.8253 | 1650 | 0.0840          | 0.9824 |
| 0.0856        | 2.9110 | 1700 | 0.0812          | 0.9841 |
| 0.0475        | 2.9966 | 1750 | 0.0955          | 0.9827 |
| 0.05          | 3.0822 | 1800 | 0.0849          | 0.9824 |
| 0.0409        | 3.1678 | 1850 | 0.0958          | 0.9812 |
| 0.0368        | 3.2534 | 1900 | 0.1069          | 0.9821 |
| 0.0423        | 3.3390 | 1950 | 0.1012          | 0.9809 |
| 0.0444        | 3.4247 | 2000 | 0.0959          | 0.9809 |
| 0.0456        | 3.5103 | 2050 | 0.0923          | 0.9806 |
| 0.0518        | 3.5959 | 2100 | 0.0985          | 0.9821 |
| 0.029         | 3.6815 | 2150 | 0.1144          | 0.9818 |
| 0.0453        | 3.7671 | 2200 | 0.0975          | 0.9809 |
| 0.0452        | 3.8527 | 2250 | 0.1031          | 0.9815 |
| 0.0369        | 3.9384 | 2300 | 0.0918          | 0.9827 |
| 0.038         | 4.0240 | 2350 | 0.0907          | 0.9830 |
| 0.0329        | 4.1096 | 2400 | 0.1058          | 0.9821 |
| 0.0249        | 4.1952 | 2450 | 0.1093          | 0.9818 |
| 0.0467        | 4.2808 | 2500 | 0.1006          | 0.9803 |
| 0.0328        | 4.3664 | 2550 | 0.1100          | 0.9824 |
| 0.0314        | 4.4521 | 2600 | 0.1150          | 0.9815 |
| 0.0274        | 4.5377 | 2650 | 0.1147          | 0.9821 |
| 0.0377        | 4.6233 | 2700 | 0.1173          | 0.9824 |
| 0.0378        | 4.7089 | 2750 | 0.1169          | 0.9827 |
| 0.0228        | 4.7945 | 2800 | 0.1141          | 0.9818 |
| 0.0281        | 4.8801 | 2850 | 0.1138          | 0.9809 |
| 0.0362        | 4.9658 | 2900 | 0.1131          | 0.9809 |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0