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mcanoglu
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bigcode-starcoderbase-1b-finetuned-defect-detection

Text Classification
Transformers
TensorBoard
Safetensors
gpt_bigcode
Generated from Trainer
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text-generation-inference
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mcanoglu commited on
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End of training

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  1. README.md +20 -19
  2. model.safetensors +1 -1
  3. runs/Feb21_06-14-19_nglczrkt3t/events.out.tfevents.1708496059.nglczrkt3t.174.0 +2 -2
README.md CHANGED
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  - generated_from_trainer
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  metrics:
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  - accuracy
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- - f1
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  - precision
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  - recall
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  model-index:
@@ -19,11 +18,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5149
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- - Accuracy: 0.7523
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- - F1: 0.7482
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- - Precision: 0.7430
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- - Recall: 0.7533
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  ## Model description
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@@ -43,28 +42,30 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 2
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- - eval_batch_size: 2
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  - seed: 4711
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- - gradient_accumulation_steps: 16
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  - total_train_batch_size: 32
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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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  - mixed_precision_training: Native AMP
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | 0.7604 | 1.0 | 996 | 0.5379 | 0.7144 | 0.6627 | 0.7829 | 0.5745 |
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- | 0.4649 | 2.0 | 1992 | 0.4524 | 0.7480 | 0.7585 | 0.7129 | 0.8104 |
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- | 0.318 | 3.0 | 2988 | 0.5149 | 0.7523 | 0.7482 | 0.7430 | 0.7533 |
 
 
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  ### Framework versions
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- - Transformers 4.36.2
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- - Pytorch 2.1.2+cu121
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- - Datasets 2.16.1
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- - Tokenizers 0.15.0
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
 
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  - precision
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  - recall
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  model-index:
 
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  This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.9591
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+ - Accuracy: 0.7666
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+ - Roc Auc: 0.7662
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+ - Precision: 0.7657
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+ - Recall: 0.7523
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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  - seed: 4711
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+ - gradient_accumulation_steps: 4
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  - total_train_batch_size: 32
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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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  - mixed_precision_training: Native AMP
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Roc Auc | Precision | Recall |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|:---------:|:------:|
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+ | 0.7596 | 1.0 | 996 | 0.5406 | 0.6852 | 0.6897 | 0.6264 | 0.8813 |
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+ | 0.4855 | 2.0 | 1993 | 0.4691 | 0.7377 | 0.7396 | 0.6954 | 0.8237 |
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+ | 0.3547 | 3.0 | 2989 | 0.4832 | 0.7480 | 0.7479 | 0.7410 | 0.7441 |
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+ | 0.2463 | 4.0 | 3986 | 0.5966 | 0.7628 | 0.7646 | 0.7196 | 0.8428 |
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+ | 0.1633 | 5.0 | 4980 | 0.9591 | 0.7666 | 0.7662 | 0.7657 | 0.7523 |
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  ### Framework versions
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+ - Transformers 4.37.2
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+ - Pytorch 2.2.0+cu121
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+ - Datasets 2.17.1
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+ - Tokenizers 0.15.2
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