Instructions to use ebinna/mamba_text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ebinna/mamba_text_classification with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ebinna/mamba_text_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
mamba_text_classification
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3443
- Flat Accuracy: 0.8735
- Accuracy: 0.0308
- Precision: 1.0
- Recall: 0.0263
- F1 Score: 0.0513
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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Flat Accuracy | Accuracy | Precision | Recall | F1 Score |
|---|---|---|---|---|---|---|---|---|
| 0.4211 | 0.25 | 100 | 0.4250 | 0.8638 | 0.0051 | 0.0769 | 0.0044 | 0.0083 |
| 0.3742 | 0.5 | 200 | 0.3627 | 0.8712 | 0.0103 | 1.0 | 0.0088 | 0.0174 |
| 0.3054 | 0.75 | 300 | 0.3526 | 0.8718 | 0.0154 | 1.0 | 0.0132 | 0.0260 |
| 0.522 | 1.0 | 400 | 0.3443 | 0.8735 | 0.0308 | 1.0 | 0.0263 | 0.0513 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu118
- Datasets 3.1.0
- Tokenizers 0.19.1
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