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mistral_yelp_review_classifier
bkeene/mistral_yelp_review_classifier
mistral_yelp_review_classifier is a text classification model from bkeene. 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
- mistral
- Access
- Public
- Created
- Oct 16, 2025
- Updated
- Oct 21, 2025
- SHA
- b82168a3
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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. -->
mistral_yelp_review_classifier
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.9634
- Accuracy: 0.433
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: 1e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 26.228 | 1.0 | 63 | 4.6814 | 0.286 |
| 16.6514 | 2.0 | 126 | 3.0909 | 0.403 |
| 10.7124 | 3.0 | 189 | 2.9634 | 0.433 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.6.0+cu124
- Datasets 4.2.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- mistral
- License
- apache-2.0
- Created
- Oct 16, 2025
- Updated
- Oct 21, 2025