---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-prompt-classifier
  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-prompt-classifier

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: 0.0380
- Accuracy: 0.9916

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.0722        | 1.0   | 16935  | 0.0490          | 0.9831   |
| 0.0509        | 2.0   | 33870  | 0.0559          | 0.9870   |
| 0.0337        | 3.0   | 50805  | 0.0518          | 0.9871   |
| 0.0228        | 4.0   | 67740  | 0.0452          | 0.9891   |
| 0.0207        | 5.0   | 84675  | 0.0521          | 0.9898   |
| 0.0154        | 6.0   | 101610 | 0.0450          | 0.9900   |
| 0.0131        | 7.0   | 118545 | 0.0555          | 0.9905   |
| 0.0112        | 8.0   | 135480 | 0.0468          | 0.9908   |
| 0.0116        | 9.0   | 152415 | 0.0578          | 0.9906   |
| 0.0073        | 10.0  | 169350 | 0.0612          | 0.9908   |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2