---
license: apache-2.0
base_model: distilbert/distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-cased-lft
  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. -->

# distilbert-cased-lft

This model is a fine-tuned version of [distilbert/distilbert-base-cased](https://huggingface.co/distilbert/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1017
- Precision: 0.8722
- Recall: 0.8905
- F1: 0.8813
- Accuracy: 0.9764

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.4065 | 100  | 0.1106          | 0.8374    | 0.7969 | 0.8167 | 0.9647   |
| No log        | 0.8130 | 200  | 0.0926          | 0.8242    | 0.8474 | 0.8356 | 0.9690   |
| No log        | 1.2195 | 300  | 0.0898          | 0.8325    | 0.8671 | 0.8494 | 0.9704   |
| No log        | 1.6260 | 400  | 0.0873          | 0.8591    | 0.8614 | 0.8602 | 0.9729   |
| 0.0943        | 2.0325 | 500  | 0.0829          | 0.8563    | 0.8765 | 0.8662 | 0.9740   |
| 0.0943        | 2.4390 | 600  | 0.0864          | 0.8656    | 0.8747 | 0.8701 | 0.9746   |
| 0.0943        | 2.8455 | 700  | 0.0842          | 0.8652    | 0.8761 | 0.8706 | 0.9746   |
| 0.0943        | 3.2520 | 800  | 0.0875          | 0.8627    | 0.8823 | 0.8724 | 0.9746   |
| 0.0943        | 3.6585 | 900  | 0.0887          | 0.8564    | 0.8829 | 0.8694 | 0.9744   |
| 0.0444        | 4.0650 | 1000 | 0.0875          | 0.8801    | 0.8797 | 0.8799 | 0.9763   |
| 0.0444        | 4.4715 | 1100 | 0.0944          | 0.8516    | 0.8901 | 0.8704 | 0.9746   |
| 0.0444        | 4.8780 | 1200 | 0.0906          | 0.8607    | 0.8891 | 0.8746 | 0.9752   |
| 0.0444        | 5.2846 | 1300 | 0.0934          | 0.8706    | 0.8896 | 0.8800 | 0.9765   |
| 0.0444        | 5.6911 | 1400 | 0.0914          | 0.8784    | 0.8862 | 0.8823 | 0.9765   |
| 0.0248        | 6.0976 | 1500 | 0.0918          | 0.8796    | 0.8896 | 0.8846 | 0.9772   |
| 0.0248        | 6.5041 | 1600 | 0.0960          | 0.8711    | 0.8916 | 0.8812 | 0.9765   |
| 0.0248        | 6.9106 | 1700 | 0.0970          | 0.8678    | 0.8876 | 0.8776 | 0.9763   |
| 0.0248        | 7.3171 | 1800 | 0.1008          | 0.8690    | 0.8887 | 0.8787 | 0.9759   |
| 0.0248        | 7.7236 | 1900 | 0.1012          | 0.8650    | 0.8926 | 0.8786 | 0.9759   |
| 0.0153        | 8.1301 | 2000 | 0.1002          | 0.8715    | 0.8921 | 0.8817 | 0.9762   |
| 0.0153        | 8.5366 | 2100 | 0.1003          | 0.8749    | 0.8889 | 0.8818 | 0.9763   |
| 0.0153        | 8.9431 | 2200 | 0.1015          | 0.8680    | 0.8917 | 0.8797 | 0.9760   |
| 0.0153        | 9.3496 | 2300 | 0.1015          | 0.8716    | 0.8882 | 0.8798 | 0.9764   |
| 0.0153        | 9.7561 | 2400 | 0.1017          | 0.8722    | 0.8905 | 0.8813 | 0.9764   |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1