Instructions to use NTA1802/mamba_text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTA1802/mamba_text_classification with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NTA1802/mamba_text_classification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| 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 an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2171 | |
| - Accuracy: 0.9474 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.0406 | 0.1 | 625 | 0.2821 | 0.9194 | | |
| | 2.0955 | 0.2 | 1250 | 0.2444 | 0.9264 | | |
| | 0.0161 | 0.3 | 1875 | 0.2228 | 0.9246 | | |
| | 0.003 | 0.4 | 2500 | 0.2371 | 0.9328 | | |
| | 0.0004 | 0.5 | 3125 | 0.2527 | 0.937 | | |
| | 2.4158 | 0.6 | 3750 | 0.2337 | 0.938 | | |
| | 0.0248 | 0.7 | 4375 | 0.2337 | 0.9476 | | |
| | 0.0012 | 0.8 | 5000 | 0.2227 | 0.9464 | | |
| | 0.0136 | 0.9 | 5625 | 0.2211 | 0.946 | | |
| | 0.024 | 1.0 | 6250 | 0.2171 | 0.9474 | | |
| ### Framework versions | |
| - Transformers 4.38.2 | |
| - Pytorch 2.2.1+cu121 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 | |