Downloads · 30 days
5
6% of all-time downloads
distilbert-base-uncased-finetuned-final
tjacquot/distilbert-base-uncased-finetuned-final
distilbert-base-uncased-finetuned-final is a text classification model from tjacquot. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
83
Public
Parameters
67M
6.4 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors268 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Dec 5, 2025
- Updated
- Dec 5, 2025
- SHA
- f1b6afc5
Try a prompt
Base models
Model card
From the Hugging Face model README
<!-- 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-final
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4175
- Precision: 0.8939
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: 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
- training_steps: 240
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision |
|---|---|---|---|---|
| 0.041 | 0.1271 | 15 | 0.5138 | 0.9186 |
| 0.0264 | 0.2542 | 30 | 0.4652 | 0.9064 |
| 0.0666 | 0.3814 | 45 | 0.4839 | 0.9137 |
| 0.0318 | 0.5085 | 60 | 0.4048 | 0.8930 |
| 0.0174 | 0.6356 | 75 | 0.4200 | 0.8797 |
| 0.0482 | 0.7627 | 90 | 0.5550 | 0.8514 |
| 0.0338 | 0.8898 | 105 | 0.5001 | 0.9160 |
| 0.0166 | 1.0169 | 120 | 0.4397 | 0.8908 |
| 0.022 | 1.1441 | 135 | 0.4291 | 0.8979 |
| 0.0055 | 1.2712 | 150 | 0.4433 | 0.8990 |
| 0.026 | 1.3983 | 165 | 0.5158 | 0.8689 |
| 0.0278 | 1.5254 | 180 | 0.4490 | 0.9019 |
| 0.0736 | 1.6525 | 195 | 0.4308 | 0.8925 |
| 0.0306 | 1.7797 | 210 | 0.4223 | 0.8925 |
| 0.0473 | 1.9068 | 225 | 0.4191 | 0.8932 |
| 0.0428 | 2.0339 | 240 | 0.4175 | 0.8939 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.1+cu128
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Dec 5, 2025
- Updated
- Dec 5, 2025