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---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased
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
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:-----:|:---------------:|
| 0.0273 | 0.2907 | 500 | 0.0062 |
| 0.0049 | 0.5814 | 1000 | 0.0024 |
| 0.0033 | 0.8721 | 1500 | 0.0020 |
| 0.0014 | 1.1628 | 2000 | 0.0009 |
| 0.001 | 1.4535 | 2500 | 0.0007 |
| 0.0008 | 1.7442 | 3000 | 0.0009 |
| 0.0011 | 2.0349 | 3500 | 0.0011 |
| 0.0003 | 2.3256 | 4000 | 0.0012 |
| 0.0008 | 2.6163 | 4500 | 0.0008 |
| 0.0006 | 2.9070 | 5000 | 0.0010 |
| 0.0006 | 3.1977 | 5500 | 0.0009 |
| 0.0002 | 3.4884 | 6000 | 0.0008 |
| 0.0005 | 3.7791 | 6500 | 0.0005 |
| 0.0003 | 4.0698 | 7000 | 0.0005 |
| 0.0002 | 4.3605 | 7500 | 0.0003 |
| 0.0004 | 4.6512 | 8000 | 0.0015 |
| 0.0004 | 4.9419 | 8500 | 0.0008 |
| 0.0002 | 5.2326 | 9000 | 0.0002 |
| 0.0003 | 5.5233 | 9500 | 0.0003 |
| 0.0002 | 5.8140 | 10000 | 0.0002 |
| 0.0002 | 6.1047 | 10500 | 0.0003 |
| 0.0001 | 6.3953 | 11000 | 0.0002 |
| 0.0001 | 6.6860 | 11500 | 0.0002 |
| 0.0001 | 6.9767 | 12000 | 0.0003 |
| 0.0 | 7.2674 | 12500 | 0.0002 |
| 0.0 | 7.5581 | 13000 | 0.0009 |
| 0.0001 | 7.8488 | 13500 | 0.0005 |
| 0.0002 | 8.1395 | 14000 | 0.0007 |
| 0.0001 | 8.4302 | 14500 | 0.0007 |
| 0.0001 | 8.7209 | 15000 | 0.0006 |
| 0.0001 | 9.0116 | 15500 | 0.0005 |
| 0.0 | 9.3023 | 16000 | 0.0007 |
| 0.0001 | 9.5930 | 16500 | 0.0005 |
| 0.0003 | 9.8837 | 17000 | 0.0004 |
| 0.0 | 10.1744 | 17500 | 0.0004 |
| 0.0002 | 10.4651 | 18000 | 0.0003 |
| 0.0 | 10.7558 | 18500 | 0.0003 |
| 0.0 | 11.0465 | 19000 | 0.0004 |
| 0.0 | 11.3372 | 19500 | 0.0004 |
| 0.0 | 11.6279 | 20000 | 0.0002 |
| 0.0 | 11.9186 | 20500 | 0.0002 |
| 0.0 | 12.2093 | 21000 | 0.0003 |
| 0.0 | 12.5 | 21500 | 0.0003 |
| 0.0 | 12.7907 | 22000 | 0.0004 |
| 0.0 | 13.0814 | 22500 | 0.0002 |
| 0.0001 | 13.3721 | 23000 | 0.0002 |
| 0.0001 | 13.6628 | 23500 | 0.0002 |
| 0.0 | 13.9535 | 24000 | 0.0002 |
| 0.0 | 14.2442 | 24500 | 0.0002 |
| 0.0 | 14.5349 | 25000 | 0.0002 |
| 0.0 | 14.8256 | 25500 | 0.0002 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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