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MiniLM-L12-H384-uncased_finetune_ag_news
HFworkshop/MiniLM-L12-H384-uncased_finetune_ag_news
MiniLM-L12-H384-uncased_finetune_ag_news is a text classification model from HFworkshop. Use it when you need a label for a piece of text. It is set up for transformers. 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
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Apr 27, 2026
- Updated
- Apr 27, 2026
- SHA
- cabd4342
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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. -->
MiniLM-L12-H384-uncased_finetune_ag_news
This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4853
- Accuracy: 0.8775
- F1: 0.8774
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: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.5476 | 1.0 | 1000 | 0.4808 | 0.857 | 0.8578 |
| 0.4042 | 2.0 | 2000 | 0.4500 | 0.877 | 0.8773 |
| 0.3163 | 3.0 | 3000 | 0.4853 | 0.8775 | 0.8774 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Apr 27, 2026
- Updated
- Apr 27, 2026