---
base_model: textattack/albert-base-v2-imdb
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: albert-tune
  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. -->

# albert-tune

This model is a fine-tuned version of [textattack/albert-base-v2-imdb](https://huggingface.co/textattack/albert-base-v2-imdb) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8791
- Accuracy: 0.6857
- F1: 0.7039

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.8887        | 1.0   | 53   | 1.8444          | 0.1929   | 0.1619 |
| 1.6055        | 2.0   | 106  | 1.5226          | 0.5929   | 0.5821 |
| 1.2048        | 3.0   | 159  | 1.1546          | 0.5857   | 0.5779 |
| 0.7243        | 4.0   | 212  | 0.9967          | 0.6214   | 0.6173 |
| 0.6455        | 5.0   | 265  | 0.9122          | 0.6857   | 0.6941 |
| 0.9166        | 6.0   | 318  | 0.8791          | 0.6857   | 0.7039 |
| 0.505         | 7.0   | 371  | 1.0556          | 0.6643   | 0.6665 |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1