---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: albert-imdb
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: imdb
      type: imdb
      config: plain_text
      split: test
      args: plain_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.94816
    - name: F1
      type: f1
      value: 0.9481598672892603
    - name: Precision
      type: precision
      value: 0.9481645892053935
    - name: Recall
      type: recall
      value: 0.94816
---

<!-- 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-imdb

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co./albert-base-v2) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1571
- Accuracy: 0.9482
- F1: 0.9482
- Precision: 0.9482
- Recall: 0.9482

## 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: 16
- eval_batch_size: 16
- seed: 9072
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3213        | 1.0   | 1563 | 0.2683          | 0.8922   | 0.8921 | 0.8930    | 0.8922 |
| 0.2211        | 2.0   | 3126 | 0.2559          | 0.9082   | 0.9082 | 0.9082    | 0.9082 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2