---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: NLPGroupProject-Finetune-DistilBert
  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. -->

# NLPGroupProject-Finetune-DistilBert

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: 1.1391
- Accuracy: 0.723

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.25  | 250  | 0.9720          | 0.69     |
| 0.9562        | 0.5   | 500  | 0.8417          | 0.707    |
| 0.9562        | 0.75  | 750  | 0.7335          | 0.73     |
| 0.8908        | 1.0   | 1000 | 0.7306          | 0.739    |
| 0.8908        | 1.25  | 1250 | 0.7490          | 0.721    |
| 0.646         | 1.5   | 1500 | 0.7560          | 0.738    |
| 0.646         | 1.75  | 1750 | 0.7759          | 0.73     |
| 0.6244        | 2.0   | 2000 | 0.8180          | 0.723    |
| 0.6244        | 2.25  | 2250 | 1.0023          | 0.722    |
| 0.359         | 2.5   | 2500 | 1.0590          | 0.728    |
| 0.359         | 2.75  | 2750 | 1.0733          | 0.723    |
| 0.3716        | 3.0   | 3000 | 1.1391          | 0.723    |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.2+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1