Instructions to use vubacktracking/mamba_text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vubacktracking/mamba_text_classification with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vubacktracking/mamba_text_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,950 Bytes
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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 for Text Classification
This model was trained from scratch on IMDB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1901
- Accuracy: 0.9536
It achieves the following results on the evaluation set:
- Loss: 0.1981
- Accuracy: 0.94
## Model description
Mamba model for text classification
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0205 | 0.1 | 625 | 0.2462 | 0.928 |
| 0.671 | 0.2 | 1250 | 0.1958 | 0.9408 |
| 0.5961 | 0.3 | 1875 | 0.2661 | 0.9344 |
| 0.0167 | 0.4 | 2500 | 0.2171 | 0.9412 |
| 0.0007 | 0.5 | 3125 | 0.2095 | 0.9448 |
| 2.6807 | 0.6 | 3750 | 0.1888 | 0.9492 |
| 0.0155 | 0.7 | 4375 | 0.2249 | 0.95 |
| 0.0021 | 0.8 | 5000 | 0.1991 | 0.9528 |
| 0.0134 | 0.9 | 5625 | 0.1920 | 0.9524 |
| 0.1525 | 1.0 | 6250 | 0.1901 | 0.9536 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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