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---
base_model: state-spaces/mamba-130m-hf
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: mamba-text-classification-v3
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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/date3k2/text-classification-imdb/runs/x4pjguay)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/date3k2/text-classification-imdb/runs/x4pjguay)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/date3k2/text-classification-imdb/runs/x4pjguay)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/date3k2/text-classification-imdb/runs/x4pjguay)
# mamba-text-classification-v3
This model is a fine-tuned version of [state-spaces/mamba-130m-hf](https://huggingface.co/state-spaces/mamba-130m-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3637
- Accuracy: 0.9454
- F1: 0.9454
- Recall: 0.9461
- Precision: 0.9447
## 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: 8
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.1731 | 0.9997 | 781 | 0.1553 | 0.9425 | 0.9427 | 0.9462 | 0.9393 |
| 0.1316 | 1.9994 | 1562 | 0.1970 | 0.9319 | 0.9294 | 0.8974 | 0.9639 |
| 0.0224 | 2.9990 | 2343 | 0.3137 | 0.9454 | 0.9455 | 0.9479 | 0.9432 |
| 0.0002 | 4.0 | 3125 | 0.3501 | 0.9449 | 0.9450 | 0.9470 | 0.9431 |
| 0.0004 | 4.9984 | 3905 | 0.3637 | 0.9454 | 0.9454 | 0.9461 | 0.9447 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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