metadata
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mamba_text_classification
results: []
mamba_text_classification
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2292
- 1: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 1}
- 4: {'precision': 0.6666666666666666, 'recall': 1.0, 'f1-score': 0.8, 'support': 2}
- 5: {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 1}
- 6: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 3}
- 9: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2}
- 10: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 2}
- Accuracy: 0.9091
- Macro avg: {'precision': 0.7777777777777777, 'recall': 0.8333333333333334, 'f1-score': 0.7999999999999999, 'support': 11}
- Weighted avg: {'precision': 0.8484848484848484, 'recall': 0.9090909090909091, 'f1-score': 0.8727272727272727, 'support': 11}
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | 0 | 1 | 4 | 5 | 6 | 9 | 10 | Accuracy | Macro avg | Weighted avg |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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} |
| 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} | |
| 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} | |
| 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} | |
| 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} | |
| 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} | |
| 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} | |
| 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} | |
| 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} |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2