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sent-an-classifier
Gudle1fr/sent-an-classifier
sent-an-classifier is a text classification model from Gudle1fr. 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
- bert
- Access
- Public
- Created
- Sep 10, 2025
- Updated
- Sep 10, 2025
- SHA
- 123c3480
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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. -->
sent-an-classifier
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2813
- Accuracy: 0.9156
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: 8
- eval_batch_size: 8
- 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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6996 | 1.0 | 30000 | 0.6927 | 0.4997 |
| 0.6946 | 2.0 | 60000 | 0.6973 | 0.4987 |
| 0.6958 | 3.0 | 90000 | 0.6942 | 0.5013 |
| 0.6942 | 4.0 | 120000 | 0.6931 | 0.4987 |
| 0.2465 | 5.0 | 150000 | 0.2813 | 0.9156 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.9.0.dev20250901+cu129
- Datasets 4.0.0
- Tokenizers 0.22.0
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Sep 10, 2025
- Updated
- Sep 10, 2025