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distilbertScenario5-news-classifier
AbrarAbhinaya/distilbertScenario5-news-classifier
distilbertScenario5-news-classifier is a text classification model from AbrarAbhinaya. 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. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Oct 30, 2025
- Updated
- Nov 2, 2025
- SHA
- 97a65287
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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. -->
distilbertScenario5-news-classifier
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: 2.2898
- Accuracy: 0.6553
- Precision: 0.7021
- Recall: 0.6553
- F1: 0.6658
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: 1.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: cosine
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 3.694 | 1.0 | 10477 | 2.8057 | 0.5292 | 0.5584 | 0.5292 | 0.5258 |
| 2.6813 | 2.0 | 20954 | 2.4632 | 0.5921 | 0.6433 | 0.5921 | 0.6003 |
| 2.4472 | 3.0 | 31431 | 2.3713 | 0.6124 | 0.6740 | 0.6124 | 0.6270 |
| 2.3234 | 4.0 | 41908 | 2.3361 | 0.6259 | 0.6794 | 0.6259 | 0.6368 |
| 2.2223 | 5.0 | 52385 | 2.3050 | 0.6397 | 0.6894 | 0.6397 | 0.6502 |
| 2.1393 | 6.0 | 62862 | 2.2754 | 0.6517 | 0.6960 | 0.6517 | 0.6614 |
| 2.071 | 7.0 | 73339 | 2.2747 | 0.6492 | 0.6994 | 0.6492 | 0.6595 |
| 2.0146 | 8.0 | 83816 | 2.2900 | 0.6511 | 0.7028 | 0.6511 | 0.6631 |
| 1.9789 | 9.0 | 94293 | 2.2902 | 0.6534 | 0.7026 | 0.6534 | 0.6641 |
| 1.9587 | 10.0 | 104770 | 2.2898 | 0.6553 | 0.7021 | 0.6553 | 0.6658 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Oct 30, 2025
- Updated
- Nov 2, 2025