Instructions to use bmd1905/mamba_text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bmd1905/mamba_text_classification with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bmd1905/mamba_text_classification", device_map="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: 0.2351 | |
| - Accuracy: 0.9406 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.0061 | 0.1 | 625 | 0.2807 | 0.9074 | | |
| | 1.7561 | 0.2 | 1250 | 0.2201 | 0.9252 | | |
| | 0.0296 | 0.3 | 1875 | 0.3153 | 0.9184 | | |
| | 0.0057 | 0.4 | 2500 | 0.2213 | 0.9342 | | |
| | 0.0002 | 0.5 | 3125 | 0.2311 | 0.935 | | |
| | 2.8004 | 0.6 | 3750 | 0.2264 | 0.9378 | | |
| | 0.0143 | 0.7 | 4375 | 0.2599 | 0.9348 | | |
| | 0.0021 | 0.8 | 5000 | 0.2267 | 0.939 | | |
| | 0.2814 | 0.9 | 5625 | 0.2343 | 0.942 | | |
| | 0.069 | 1.0 | 6250 | 0.2351 | 0.9406 | | |
| ### Framework versions | |
| - Transformers 4.38.1 | |
| - Pytorch 2.1.1+cu118 | |
| - Datasets 2.17.1 | |
| - Tokenizers 0.15.2 | |