End of training

README.md

@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5530
-- Accuracy: 0.7686
-- F1: 0.7679
-- Precision: 0.7527
-- Recall: 0.7837
+- Loss: 0.8154
+- Accuracy: 0.7877
+- F1: 0.7861
+- Precision: 0.7736
+- Recall: 0.7991
 
 ## Model description
 
@@ -44,28 +44,30 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 2
-- eval_batch_size: 2
+- train_batch_size: 8
+- eval_batch_size: 8
 - seed: 4711
-- gradient_accumulation_steps: 16
+- gradient_accumulation_steps: 4
 - total_train_batch_size: 32
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 3
+- num_epochs: 5
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| 0.5818        | 1.0   | 996  | 0.4514          | 0.7510   | 0.7353 | 0.7647    | 0.7081 |
-| 0.3357        | 2.0   | 1992 | 0.4397          | 0.7615   | 0.7695 | 0.7289    | 0.8150 |
-| 0.2098        | 3.0   | 2988 | 0.5530          | 0.7686   | 0.7679 | 0.7527    | 0.7837 |
+| 0.5701        | 1.0   | 996  | 0.4446          | 0.7417   | 0.7633 | 0.6910    | 0.8525 |
+| 0.3448        | 2.0   | 1993 | 0.4246          | 0.7681   | 0.7490 | 0.7944    | 0.7086 |
+| 0.2305        | 3.0   | 2989 | 0.4693          | 0.7912   | 0.7924 | 0.7701    | 0.8160 |
+| 0.1564        | 4.0   | 3986 | 0.5977          | 0.7836   | 0.7790 | 0.7774    | 0.7806 |
+| 0.1102        | 5.0   | 4980 | 0.8154          | 0.7877   | 0.7861 | 0.7736    | 0.7991 |
 
 
 ### Framework versions
 
-- Transformers 4.36.2
-- Pytorch 2.1.2+cu121
-- Datasets 2.16.1
-- Tokenizers 0.15.0
+- Transformers 4.38.0
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.1
+- Tokenizers 0.15.2

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