transformers pytorch albert text-classification classifier generated_from_trainer dataset:ag_news license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- ag_news
license: apache-2.0
metrics:
- accuracy
tags:
- classifier
- generated_from_trainer
model-index:
- name: deep_model_09_clasificador-news-2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: ag_news
      type: ag_news
      config: default
      split: test
      args: default
    metrics:
    - type: accuracy
      value: 0.9033149171270718
      name: Accuracy
---

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# deep_model_09_clasificador-news-2

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4530
- Accuracy: 0.9033

## 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
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6332        | 1.0   | 715  | 0.4676          | 0.8812   |
| 0.5148        | 2.0   | 1430 | 0.4496          | 0.9006   |
| 0.3638        | 3.0   | 2145 | 0.4530          | 0.9033   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
