---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-pad-clf
  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-base-uncased-finetuned-pad-clf

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: 0.1289
- Accuracy: 0.975
- F1: 0.9749

## 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: 256
- eval_batch_size: 256
- 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 | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 4    | 0.6435          | 0.6083   | 0.5142 |
| No log        | 2.0   | 8    | 0.5480          | 0.9083   | 0.9081 |
| No log        | 3.0   | 12   | 0.4297          | 0.9417   | 0.9417 |
| 0.5756        | 4.0   | 16   | 0.3197          | 0.9667   | 0.9666 |
| 0.5756        | 5.0   | 20   | 0.2381          | 0.9667   | 0.9665 |
| 0.5756        | 6.0   | 24   | 0.1897          | 0.975    | 0.9749 |
| 0.5756        | 7.0   | 28   | 0.1611          | 0.975    | 0.9749 |
| 0.2307        | 8.0   | 32   | 0.1431          | 0.975    | 0.9749 |
| 0.2307        | 9.0   | 36   | 0.1328          | 0.975    | 0.9749 |
| 0.2307        | 10.0  | 40   | 0.1289          | 0.975    | 0.9749 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0