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tarekxpc
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mamba_text_classification

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PyTorch
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tarekxpc commited on
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  1. README.md +24 -16
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -15,8 +15,16 @@ should probably proofread and complete it, then remove this comment. -->
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  This model was trained from scratch on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6274
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- - Accuracy: 1.0
 
 
 
 
 
 
 
 
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  ## Model description
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@@ -35,9 +43,9 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 1e-06
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  - train_batch_size: 4
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- - eval_batch_size: 8
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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: cosine
@@ -46,22 +54,22 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.6169 | 0.4 | 52 | 0.8997 | 0.0 |
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- | 0.698 | 0.81 | 104 | 0.7669 | 0.0 |
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- | 0.5953 | 1.21 | 156 | 0.6956 | 0.0 |
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- | 0.5979 | 1.61 | 208 | 0.6580 | 1.0 |
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- | 0.5949 | 2.02 | 260 | 0.6465 | 1.0 |
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- | 0.6608 | 2.42 | 312 | 0.6321 | 1.0 |
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- | 0.5082 | 2.82 | 364 | 0.6339 | 1.0 |
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- | 0.578 | 3.22 | 416 | 0.6302 | 1.0 |
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- | 0.6325 | 3.63 | 468 | 0.6274 | 1.0 |
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  ### Framework versions
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  - Transformers 4.38.2
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  - Pytorch 2.2.1+cu121
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- - Datasets 2.18.0
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  - Tokenizers 0.15.2
 
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  This model was trained from scratch on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2292
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+ - 1: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1}
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+ - 4: {'precision': 0.6666666666666666, 'recall': 1.0, 'f1-score': 0.8, 'support': 2}
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+ - 5: {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 1}
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+ - 6: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 3}
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+ - 9: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2}
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+ - 10: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2}
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+ - Accuracy: 0.9091
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+ - Macro avg: {'precision': 0.7777777777777777, 'recall': 0.8333333333333334, 'f1-score': 0.7999999999999999, 'support': 11}
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+ - Weighted avg: {'precision': 0.8484848484848484, 'recall': 0.9090909090909091, 'f1-score': 0.8727272727272727, 'support': 11}
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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: 5e-05
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  - train_batch_size: 4
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+ - eval_batch_size: 16
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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: cosine
 
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | 0 | 1 | 4 | 5 | 6 | 9 | 10 | Accuracy | Macro avg | Weighted avg |
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+ |:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------:|:----------------------------------------------------------------:|:-------------------------------------------------------------------------------:|:----------------------------------------------------------------:|:-------------------------------------------------------------------------------:|:----------------------------------------------------------------:|:----------------------------------------------------------------:|:--------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|
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+ | 1.0038 | 0.4 | 459 | 0.7923 | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 0} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1-score': 0.8, 'support': 2} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 1} | {'precision': 1.0, 'recall': 0.6666666666666666, 'f1-score': 0.8, 'support': 3} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | 0.8182 | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1-score': 0.6571428571428571, 'support': 11} | {'precision': 0.8484848484848484, 'recall': 0.8181818181818182, 'f1-score': 0.8181818181818182, 'support': 11} |
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+ | 1.0341 | 0.8 | 918 | 0.0965 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 3} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | 1.0 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 11}| {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 11} |
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+ | 0.0006 | 1.2 | 1377 | 0.1084 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1-score': 0.8, 'support': 2}| {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 1} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 3} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | 0.9091 | {'precision': 0.7777777777777777, 'recall': 0.8333333333333334, 'f1-score': 0.7999999999999999, 'support': 11}| {'precision': 0.8484848484848484, 'recall': 0.9090909090909091, 'f1-score': 0.8727272727272727, 'support': 11} |
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+ | 0.1193 | 1.6 | 1836 | 0.7853 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1-score': 0.8, 'support': 2}| {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 1} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 3} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | 0.9091 | {'precision': 0.7777777777777777, 'recall': 0.8333333333333334, 'f1-score': 0.7999999999999999, 'support': 11}| {'precision': 0.8484848484848484, 'recall': 0.9090909090909091, 'f1-score': 0.8727272727272727, 'support': 11} |
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+ | 0.007 | 2.0 | 2295 | 0.0076 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 3} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | 1.0 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 11}| {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 11} |
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+ | 0.0001 | 2.4 | 2754 | 0.3204 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1-score': 0.8, 'support': 2}| {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 1} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 3} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | 0.9091 | {'precision': 0.7777777777777777, 'recall': 0.8333333333333334, 'f1-score': 0.7999999999999999, 'support': 11}| {'precision': 0.8484848484848484, 'recall': 0.9090909090909091, 'f1-score': 0.8727272727272727, 'support': 11} |
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+ | 0.0001 | 2.8 | 3213 | 0.0948 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1-score': 0.8, 'support': 2}| {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 1} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 3} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | 0.9091 | {'precision': 0.7777777777777777, 'recall': 0.8333333333333334, 'f1-score': 0.7999999999999999, 'support': 11}| {'precision': 0.8484848484848484, 'recall': 0.9090909090909091, 'f1-score': 0.8727272727272727, 'support': 11} |
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+ | 0.0001 | 3.2 | 3672 | 0.1412 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1-score': 0.8, 'support': 2}| {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 1} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 3} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | 0.9091 | {'precision': 0.7777777777777777, 'recall': 0.8333333333333334, 'f1-score': 0.7999999999999999, 'support': 11}| {'precision': 0.8484848484848484, 'recall': 0.9090909090909091, 'f1-score': 0.8727272727272727, 'support': 11} |
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+ | 0.0 | 3.6 | 4131 | 0.2292 | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1-score': 0.8, 'support': 2}| {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 1} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 3} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2} | 0.9091 | {'precision': 0.7777777777777777, 'recall': 0.8333333333333334, 'f1-score': 0.7999999999999999, 'support': 11}| {'precision': 0.8484848484848484, 'recall': 0.9090909090909091, 'f1-score': 0.8727272727272727, 'support': 11} |
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  ### Framework versions
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  - Transformers 4.38.2
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  - Pytorch 2.2.1+cu121
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+ - Datasets 2.19.0
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  - Tokenizers 0.15.2
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