Instructions to use TRanHieu009/mamba_text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TRanHieu009/mamba_text_classification with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TRanHieu009/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: 0.2144 | |
| - Accuracy: 0.944 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.0088 | 0.1 | 625 | 0.2663 | 0.9216 | | |
| | 3.5723 | 0.2 | 1250 | 0.3047 | 0.8962 | | |
| | 1.4067 | 0.3 | 1875 | 0.2881 | 0.919 | | |
| | 0.278 | 0.4 | 2500 | 0.2252 | 0.9322 | | |
| | 0.0034 | 0.5 | 3125 | 0.2200 | 0.9382 | | |
| | 2.526 | 0.6 | 3750 | 0.2670 | 0.9354 | | |
| | 0.5528 | 0.7 | 4375 | 0.2209 | 0.9386 | | |
| | 0.0006 | 0.8 | 5000 | 0.2294 | 0.9432 | | |
| | 0.0358 | 0.9 | 5625 | 0.2167 | 0.9438 | | |
| | 0.5311 | 1.0 | 6250 | 0.2144 | 0.944 | | |
| ### Framework versions | |
| - Transformers 4.40.0 | |
| - Pytorch 2.2.1+cu121 | |
| - Datasets 2.19.0 | |
| - Tokenizers 0.19.1 | |