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Llama3_8B_Task2_semantic_pred

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rishavranaut commited on Sep 4

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rishavranaut/Llama3_8B_Task2_semantic_pred

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@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
 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.
 It achieves the following results on the evaluation set:
-- Loss: 1.0217
-- Accuracy: 0.6102
-- Precision: 0.6102
-- Recall: 0.6102
-- F1 score: 0.6102
 ## Model description
@@ -49,17 +49,21 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 3
 ### Training results
-| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1 score |
-|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
-| 0.49          | 0.5208 | 200  | 0.9015          | 0.5750   | 0.5750    | 0.5750 | 0.5750   |
-| 0.439         | 1.0417 | 400  | 1.2361          | 0.5541   | 0.5541    | 0.5541 | 0.5541   |
-| 0.2744        | 1.5625 | 600  | 0.4804          | 0.7744   | 0.7744    | 0.7744 | 0.7744   |
-| 0.2621        | 2.0833 | 800  | 1.2460          | 0.5658   | 0.5658    | 0.5658 | 0.5658   |
-| 0.1921        | 2.6042 | 1000 | 1.0217          | 0.6102   | 0.6102    | 0.6102 | 0.6102   |
 ### Framework versions

 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.
 It achieves the following results on the evaluation set:
+- Loss: 1.2767
+- Accuracy: 0.6493
+- Precision: 0.6493
+- Recall: 0.6493
+- F1 score: 0.6493
 ## Model description
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
+- num_epochs: 5
 ### Training results
+| Training Loss | Epoch  | Step | Accuracy | F1 score | Precision | Recall | Validation Loss |
+|:-------------:|:------:|:----:|:--------:|:--------:|:---------:|:------:|:---------------:|
+| 0.49          | 0.5208 | 200  | 0.5750   | 0.5750   | 0.5750    | 0.5750 | 0.9015          |
+| 0.439         | 1.0417 | 400  | 0.5541   | 0.5541   | 0.5541    | 0.5541 | 1.2361          |
+| 0.2744        | 1.5625 | 600  | 0.7744   | 0.7744   | 0.7744    | 0.7744 | 0.4804          |
+| 0.2621        | 2.0833 | 800  | 0.5658   | 0.5658   | 0.5658    | 0.5658 | 1.2460          |
+| 0.1921        | 2.6042 | 1000 | 0.6102   | 0.6102   | 0.6102    | 0.6102 | 1.0217          |
+| 0.1602        | 3.125  | 1200 | 0.5880   | 0.5880   | 0.5880    | 0.5880 | 1.3196          |
+| 0.1736        | 3.6458 | 1400 | 0.5684   | 0.5684   | 0.5684    | 0.5684 | 1.7235          |
+| 0.1628        | 4.1667 | 1600 | 0.6780   | 0.6780   | 0.6780    | 0.6780 | 1.0542          |
+| 0.1204        | 4.6875 | 1800 | 1.2767   | 0.6493   | 0.6493    | 0.6493 | 0.6493          |
 ### Framework versions

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