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End of training

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README.md CHANGED
@@ -21,8 +21,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the super_glue dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3115
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- - Accuracy: 0.8735
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  ## Model description
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@@ -44,7 +44,7 @@ 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: 1
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  - distributed_type: multi-GPU
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  - num_devices: 2
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  - gradient_accumulation_steps: 2
@@ -58,30 +58,24 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.369 | 0.05 | 50 | 0.4328 | 0.8120 |
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- | 0.2708 | 0.1 | 100 | 0.4051 | 0.8283 |
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- | 0.6276 | 0.15 | 150 | 0.4020 | 0.8452 |
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- | 0.4395 | 0.2 | 200 | 0.3671 | 0.8452 |
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- | 0.3282 | 0.25 | 250 | 0.3746 | 0.8445 |
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- | 0.2967 | 0.3 | 300 | 0.3557 | 0.8523 |
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- | 0.2483 | 0.35 | 350 | 0.3862 | 0.8622 |
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- | 0.384 | 0.4 | 400 | 0.3765 | 0.8565 |
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- | 0.334 | 0.45 | 450 | 0.3628 | 0.8601 |
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- | 0.2671 | 0.5 | 500 | 0.3290 | 0.8664 |
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- | 0.2478 | 0.55 | 550 | 0.3421 | 0.8650 |
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- | 0.1814 | 0.6 | 600 | 0.3233 | 0.8693 |
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- | 0.3332 | 0.65 | 650 | 0.3451 | 0.8728 |
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- | 0.2063 | 0.7 | 700 | 0.3709 | 0.8678 |
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- | 0.2614 | 0.75 | 750 | 0.3530 | 0.8763 |
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- | 0.4273 | 0.8 | 800 | 0.3383 | 0.8721 |
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- | 0.1319 | 0.85 | 850 | 0.3360 | 0.8735 |
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- | 0.196 | 0.9 | 900 | 0.3096 | 0.8735 |
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- | 0.3564 | 0.95 | 950 | 0.3354 | 0.8770 |
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- | 0.3145 | 1.0 | 1000 | 0.3421 | 0.8784 |
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- | 0.1344 | 1.05 | 1050 | 0.4273 | 0.8735 |
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- | 0.4227 | 1.1 | 1100 | 0.3555 | 0.8707 |
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- | 0.1696 | 1.15 | 1150 | 0.3399 | 0.8742 |
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- | 0.5423 | 1.2 | 1200 | 0.3405 | 0.8813 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the super_glue dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3297
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+ - Accuracy: 0.8700
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  ## Model description
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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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.4632 | 0.05 | 50 | 0.4840 | 0.7958 |
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+ | 0.3453 | 0.1 | 100 | 0.3888 | 0.8226 |
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+ | 0.2722 | 0.15 | 150 | 0.3590 | 0.8396 |
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+ | 0.3266 | 0.2 | 200 | 0.3811 | 0.8459 |
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+ | 0.3699 | 0.25 | 250 | 0.3534 | 0.8438 |
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+ | 0.3554 | 0.3 | 300 | 0.3378 | 0.8565 |
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+ | 0.1229 | 0.35 | 350 | 0.3368 | 0.8643 |
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+ | 0.3522 | 0.4 | 400 | 0.3424 | 0.8643 |
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+ | 0.2548 | 0.45 | 450 | 0.3467 | 0.8664 |
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+ | 0.2119 | 0.5 | 500 | 0.3439 | 0.8714 |
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+ | 0.2113 | 0.55 | 550 | 0.3518 | 0.8657 |
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+ | 0.2122 | 0.6 | 600 | 0.3110 | 0.8770 |
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+ | 0.3251 | 0.65 | 650 | 0.3323 | 0.8728 |
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+ | 0.2904 | 0.7 | 700 | 0.3152 | 0.8792 |
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+ | 0.6366 | 0.75 | 750 | 0.3502 | 0.8763 |
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+ | 0.4161 | 0.8 | 800 | 0.3250 | 0.8806 |
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+ | 0.1605 | 0.85 | 850 | 0.3258 | 0.8834 |
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+ | 0.271 | 0.9 | 900 | 0.3330 | 0.8848 |
 
 
 
 
 
 
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  ### Framework versions
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