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--- |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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datasets: |
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- super_glue |
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metrics: |
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- accuracy |
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model-index: |
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- name: mistral_sparse_80_percent_boolq_1000 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mistral_sparse_80_percent_boolq_1000 |
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This model is a fine-tuned version of [](https://huggingface.co/) on the super_glue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3381 |
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- Accuracy: 0.8664 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 4 |
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- seed: 2 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- total_eval_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.4991 | 0.05 | 50 | 0.5522 | 0.7216 | |
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| 0.3812 | 0.1 | 100 | 0.4342 | 0.8141 | |
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| 0.369 | 0.15 | 150 | 0.4112 | 0.8170 | |
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| 0.4132 | 0.2 | 200 | 0.4139 | 0.8382 | |
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| 0.4219 | 0.25 | 250 | 0.3940 | 0.8339 | |
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| 0.4144 | 0.3 | 300 | 0.3803 | 0.8481 | |
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| 0.1534 | 0.35 | 350 | 0.3786 | 0.8516 | |
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| 0.4855 | 0.4 | 400 | 0.3821 | 0.8502 | |
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| 0.2109 | 0.45 | 450 | 0.3583 | 0.8516 | |
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| 0.3026 | 0.5 | 500 | 0.3675 | 0.8558 | |
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| 0.2903 | 0.55 | 550 | 0.3744 | 0.8537 | |
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| 0.2988 | 0.6 | 600 | 0.3573 | 0.8587 | |
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| 0.3432 | 0.65 | 650 | 0.3396 | 0.8657 | |
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| 0.3156 | 0.7 | 700 | 0.3299 | 0.8671 | |
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| 0.4978 | 0.75 | 750 | 0.3623 | 0.8657 | |
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| 0.4523 | 0.8 | 800 | 0.3240 | 0.8700 | |
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| 0.2367 | 0.85 | 850 | 0.3393 | 0.8678 | |
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| 0.3334 | 0.9 | 900 | 0.3252 | 0.8834 | |
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| 0.3286 | 0.95 | 950 | 0.3605 | 0.8742 | |
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| 0.1659 | 1.0 | 1000 | 0.3269 | 0.8742 | |
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| 0.2373 | 1.05 | 1050 | 0.3256 | 0.8792 | |
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| 0.5102 | 1.1 | 1100 | 0.3633 | 0.8749 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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