Instructions to use alamgirqazi/mamba_text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alamgirqazi/mamba_text_classification with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("alamgirqazi/mamba_text_classification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
mamba_text_classification
This model was trained from scratch on IMDB dataset. It achieves the following results on the evaluation set:
- Loss: 0.2454
- Accuracy: 0.932
Installation
conda create -n mamba_env python=3.10
conda activate mamba_env
pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu122
pip install 'causal-conv1d>=1.4.0'
pip install mamba-ssm==2.2.1
pip install datasets scikit-learn
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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0109 | 0.1 | 625 | 0.3104 | 0.896 |
| 2.5826 | 0.2 | 1250 | 0.2714 | 0.912 |
| 0.0115 | 0.3 | 1875 | 0.2622 | 0.928 |
| 0.0133 | 0.4 | 2500 | 0.2427 | 0.924 |
| 0.0012 | 0.5 | 3125 | 0.2205 | 0.916 |
| 2.1868 | 0.6 | 3750 | 0.2280 | 0.936 |
| 1.5594 | 0.7 | 4375 | 0.2529 | 0.932 |
| 0.0016 | 0.8 | 5000 | 0.2196 | 0.94 |
| 0.0055 | 0.9 | 5625 | 0.2365 | 0.932 |
| 0.3351 | 1.0 | 6250 | 0.2454 | 0.932 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
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