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my-amazonreviews-finetuned-model
ambiernat/my-amazonreviews-finetuned-model
my-amazonreviews-finetuned-model is a text classification model from ambiernat. 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
- Nov 3, 2025
- Updated
- Nov 15, 2025
- SHA
- 88ef9386
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. -->
my-amazonreviews-finetuned-model
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7817
- Accuracy: 0.7
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: 16
- eval_batch_size: 32
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 82 | 0.9434 | 0.6276 |
| 1.1397 | 2.0 | 164 | 0.8340 | 0.6621 |
| 0.7992 | 3.0 | 246 | 0.8066 | 0.6966 |
| 0.6158 | 4.0 | 328 | 0.8200 | 0.6759 |
| 0.4798 | 5.0 | 410 | 0.8212 | 0.6897 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 3.6.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Nov 3, 2025
- Updated
- Nov 15, 2025