---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mamba_text_classification
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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