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mistral_sparse_80_percent_boolq_1000
thrunlab/mistral_sparse_80_percent_boolq_1000
mistral_sparse_80_percent_boolq_1000 is a machine learning model from thrunlab. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- mistral
- Access
- Public
- Created
- Feb 6, 2024
- Updated
- Feb 6, 2024
- SHA
- a3cd2c9d
Datasets
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_sparse_80_percent_boolq_1000
This model is a fine-tuned version of on the super_glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.3381
- Accuracy: 0.8664
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-05
- train_batch_size: 2
- eval_batch_size: 4
- seed: 2
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4991 | 0.05 | 50 | 0.5522 | 0.7216 |
| 0.3812 | 0.1 | 100 | 0.4342 | 0.8141 |
| 0.369 | 0.15 | 150 | 0.4112 | 0.8170 |
| 0.4132 | 0.2 | 200 | 0.4139 | 0.8382 |
| 0.4219 | 0.25 | 250 | 0.3940 | 0.8339 |
| 0.4144 | 0.3 | 300 | 0.3803 | 0.8481 |
| 0.1534 | 0.35 | 350 | 0.3786 | 0.8516 |
| 0.4855 | 0.4 | 400 | 0.3821 | 0.8502 |
| 0.2109 | 0.45 | 450 | 0.3583 | 0.8516 |
| 0.3026 | 0.5 | 500 | 0.3675 | 0.8558 |
| 0.2903 | 0.55 | 550 | 0.3744 | 0.8537 |
| 0.2988 | 0.6 | 600 | 0.3573 | 0.8587 |
| 0.3432 | 0.65 | 650 | 0.3396 | 0.8657 |
| 0.3156 | 0.7 | 700 | 0.3299 | 0.8671 |
| 0.4978 | 0.75 | 750 | 0.3623 | 0.8657 |
| 0.4523 | 0.8 | 800 | 0.3240 | 0.8700 |
| 0.2367 | 0.85 | 850 | 0.3393 | 0.8678 |
| 0.3334 | 0.9 | 900 | 0.3252 | 0.8834 |
| 0.3286 | 0.95 | 950 | 0.3605 | 0.8742 |
| 0.1659 | 1.0 | 1000 | 0.3269 | 0.8742 |
| 0.2373 | 1.05 | 1050 | 0.3256 | 0.8792 |
| 0.5102 | 1.1 | 1100 | 0.3633 | 0.8749 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
- Library
- transformers
- Type
- mistral
- Created
- Feb 6, 2024
- Updated
- Feb 6, 2024