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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: mamba_text_classification |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mamba_text_classification |
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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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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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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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- lr_scheduler_warmup_ratio: 0.01 |
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- num_epochs: 4 |
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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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