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  2. adapter_model.safetensors +1 -1
README.md CHANGED
@@ -21,11 +21,11 @@ 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-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1704
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- - Precision: 0.9275
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- - Recall: 0.9391
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  - F1: 0.9333
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- - Accuracy: 0.9868
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0001
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.0449 | 1.0 | 1756 | 0.1034 | 0.9239 | 0.9330 | 0.9284 | 0.9857 |
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- | 0.0225 | 2.0 | 3512 | 0.1098 | 0.9210 | 0.9301 | 0.9256 | 0.9853 |
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- | 0.0121 | 3.0 | 5268 | 0.1104 | 0.9276 | 0.9346 | 0.9311 | 0.9864 |
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- | 0.0057 | 4.0 | 7024 | 0.1408 | 0.9232 | 0.9370 | 0.9300 | 0.9863 |
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- | 0.0023 | 5.0 | 8780 | 0.1538 | 0.9245 | 0.9373 | 0.9309 | 0.9865 |
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- | 0.0011 | 6.0 | 10536 | 0.1660 | 0.9275 | 0.9393 | 0.9334 | 0.9868 |
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- | 0.0008 | 7.0 | 12292 | 0.1708 | 0.9283 | 0.9393 | 0.9338 | 0.9869 |
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- | 0.0006 | 8.0 | 14048 | 0.1710 | 0.9280 | 0.9395 | 0.9337 | 0.9869 |
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- | 0.0004 | 9.0 | 15804 | 0.1706 | 0.9276 | 0.9391 | 0.9333 | 0.9869 |
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- | 0.0003 | 10.0 | 17560 | 0.1704 | 0.9275 | 0.9391 | 0.9333 | 0.9868 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2046
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+ - Precision: 0.9273
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+ - Recall: 0.9393
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  - F1: 0.9333
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+ - Accuracy: 0.9864
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.0002
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0499 | 1.0 | 1756 | 0.1085 | 0.9196 | 0.9287 | 0.9241 | 0.9845 |
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+ | 0.0233 | 2.0 | 3512 | 0.0997 | 0.9249 | 0.9226 | 0.9237 | 0.9845 |
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+ | 0.0097 | 3.0 | 5268 | 0.1343 | 0.9292 | 0.9386 | 0.9339 | 0.9870 |
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+ | 0.0036 | 4.0 | 7024 | 0.1651 | 0.9245 | 0.9386 | 0.9315 | 0.9864 |
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+ | 0.0012 | 5.0 | 8780 | 0.1839 | 0.9257 | 0.9373 | 0.9315 | 0.9863 |
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+ | 0.0005 | 6.0 | 10536 | 0.2027 | 0.9258 | 0.9386 | 0.9321 | 0.9864 |
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+ | 0.0002 | 7.0 | 12292 | 0.2022 | 0.9276 | 0.9384 | 0.9330 | 0.9864 |
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+ | 0.0002 | 8.0 | 14048 | 0.2040 | 0.9274 | 0.9388 | 0.9331 | 0.9864 |
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+ | 0.0001 | 9.0 | 15804 | 0.2048 | 0.9270 | 0.9393 | 0.9331 | 0.9864 |
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+ | 0.0001 | 10.0 | 17560 | 0.2046 | 0.9273 | 0.9393 | 0.9333 | 0.9864 |
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  ### Framework versions
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