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roberta-base-product-related
SF001-123456/roberta-base-product-related
roberta-base-product-related is a text classification model from SF001-123456. Use it when you need a label for a piece of text. It is set up for peft. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- peft
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Aug 28, 2025
- Updated
- Aug 28, 2025
- SHA
- 7c418168
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. -->
roberta-base-product-related
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1125
- Accuracy: 0.9713
- Precision: 0.9559
- Recall: 0.9894
- F1: 0.9723
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: 16
- 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.1808 | 1.0 | 3360 | 0.1271 | 0.9631 | 0.9464 | 0.9834 | 0.9645 |
| 0.1033 | 2.0 | 6720 | 0.1151 | 0.9691 | 0.9521 | 0.9892 | 0.9703 |
| 0.1365 | 3.0 | 10080 | 0.1125 | 0.9713 | 0.9559 | 0.9894 | 0.9723 |
Framework versions
- PEFT 0.17.1
- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
- Task
- Text Classification
- Library
- peft
- Type
- roberta
- License
- mit
- Created
- Aug 28, 2025
- Updated
- Aug 28, 2025