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ag_news-Classification

Text Classification
Transformers
PyTorch
bert
generated_from_trainer
Eval Results
Inference Endpoints
Model card Files Community
2
ag_news-Classification
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---
license: mit
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: results
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: ag_news
      type: ag_news
      config: default
      split: train[:40000]
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8951
    - name: F1
      type: f1
      value: 0.8964447542636089
    - name: Precision
      type: precision
      value: 0.8978261707981314
    - name: Recall
      type: recall
      value: 0.896474840596734
---

<!-- 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. -->

# results

This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3320
- Accuracy: 0.8951
- F1: 0.8964
- Precision: 0.8978
- Recall: 0.8965

## 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: 0.0003
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2783        | 1.0   | 625  | 0.3046          | 0.8949   | 0.8960 | 0.8970    | 0.8963 |
| 0.1878        | 2.0   | 1250 | 0.3139          | 0.8954   | 0.8971 | 0.8995    | 0.8965 |
| 0.1311        | 3.0   | 1875 | 0.3320          | 0.8951   | 0.8964 | 0.8978    | 0.8965 |


### Framework versions

- Transformers 4.22.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1