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tarekxpc/
mamba_text_classification

Name: mamba_text_classification
Rating: 2.5 (20 reviews)

transformers

pytorch

generated_from_trainer

endpoints_compatible

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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

tarekxpc/mamba_text_classification

mamba_text_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tarekxpc/
mamba_text_classification