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minilm-fine-tuned-emotions
kim586w/minilm-fine-tuned-emotions
minilm-fine-tuned-emotions is a text classification model from kim586w. 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
- Nov 25, 2025
- Updated
- Nov 25, 2025
- SHA
- 17325a95
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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-fine-tuned-emotions
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: 0.3914
- F1: 0.9099
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: 64
- eval_batch_size: 64
- 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 | F1 |
|---|---|---|---|---|
| 1.373 | 1.0 | 250 | 1.0571 | 0.5777 |
| 0.8853 | 2.0 | 500 | 0.6926 | 0.8148 |
| 0.6016 | 3.0 | 750 | 0.4943 | 0.8829 |
| 0.4623 | 4.0 | 1000 | 0.4235 | 0.9020 |
| 0.3996 | 5.0 | 1250 | 0.3914 | 0.9099 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Nov 25, 2025
- Updated
- Nov 25, 2025