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Llama3_8B_Task2_semantic_pred

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rishavranaut/Llama3_8B_Task2_semantic_pred

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  1. README.md +17 -13
  2. adapter_model.safetensors +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0217
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- - Accuracy: 0.6102
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- - Precision: 0.6102
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- - Recall: 0.6102
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- - F1 score: 0.6102
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  ## Model description
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@@ -49,17 +49,21 @@ The following hyperparameters were used during training:
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  - seed: 42
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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: 3
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 score |
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- |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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- | 0.49 | 0.5208 | 200 | 0.9015 | 0.5750 | 0.5750 | 0.5750 | 0.5750 |
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- | 0.439 | 1.0417 | 400 | 1.2361 | 0.5541 | 0.5541 | 0.5541 | 0.5541 |
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- | 0.2744 | 1.5625 | 600 | 0.4804 | 0.7744 | 0.7744 | 0.7744 | 0.7744 |
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- | 0.2621 | 2.0833 | 800 | 1.2460 | 0.5658 | 0.5658 | 0.5658 | 0.5658 |
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- | 0.1921 | 2.6042 | 1000 | 1.0217 | 0.6102 | 0.6102 | 0.6102 | 0.6102 |
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2767
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+ - Accuracy: 0.6493
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+ - Precision: 0.6493
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+ - Recall: 0.6493
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+ - F1 score: 0.6493
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  ## Model description
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  - seed: 42
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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: 5
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  ### Training results
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+ | Training Loss | Epoch | Step | Accuracy | F1 score | Precision | Recall | Validation Loss |
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+ |:-------------:|:------:|:----:|:--------:|:--------:|:---------:|:------:|:---------------:|
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+ | 0.49 | 0.5208 | 200 | 0.5750 | 0.5750 | 0.5750 | 0.5750 | 0.9015 |
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+ | 0.439 | 1.0417 | 400 | 0.5541 | 0.5541 | 0.5541 | 0.5541 | 1.2361 |
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+ | 0.2744 | 1.5625 | 600 | 0.7744 | 0.7744 | 0.7744 | 0.7744 | 0.4804 |
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+ | 0.2621 | 2.0833 | 800 | 0.5658 | 0.5658 | 0.5658 | 0.5658 | 1.2460 |
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+ | 0.1921 | 2.6042 | 1000 | 0.6102 | 0.6102 | 0.6102 | 0.6102 | 1.0217 |
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+ | 0.1602 | 3.125 | 1200 | 0.5880 | 0.5880 | 0.5880 | 0.5880 | 1.3196 |
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+ | 0.1736 | 3.6458 | 1400 | 0.5684 | 0.5684 | 0.5684 | 0.5684 | 1.7235 |
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+ | 0.1628 | 4.1667 | 1600 | 0.6780 | 0.6780 | 0.6780 | 0.6780 | 1.0542 |
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+ | 0.1204 | 4.6875 | 1800 | 1.2767 | 0.6493 | 0.6493 | 0.6493 | 0.6493 |
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  ### Framework versions
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