transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:autextification2023 license:mit model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- autextification2023
license: mit
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: ia-detection-bert-tiny
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: autextification2023
      type: autextification2023
      config: detection_en
      split: train
      args: detection_en
    metrics:
    - type: accuracy
      value: 0.706714913887871
      name: Accuracy
    - type: f1
      value: 0.7557691574169433
      name: F1
    - type: precision
      value: 0.6592799627337459
      name: Precision
    - type: recall
      value: 0.8853440571939232
      name: Recall
---

<!-- 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. -->

# ia-detection-bert-tiny

This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the autextification2023 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0749
- Accuracy: 0.7067
- F1: 0.7558
- Precision: 0.6593
- Recall: 0.8853

## 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: 0.0001
- 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4176        | 1.0   | 3808  | 0.4391          | 0.7962   | 0.7629 | 0.8973    | 0.6635 |
| 0.2567        | 2.0   | 7616  | 0.4912          | 0.8233   | 0.8021 | 0.8984    | 0.7244 |
| 0.2342        | 3.0   | 11424 | 0.5477          | 0.8473   | 0.8355 | 0.8932    | 0.7848 |
| 0.2226        | 4.0   | 15232 | 0.7703          | 0.8059   | 0.7743 | 0.9103    | 0.6736 |
| 0.2706        | 5.0   | 19040 | 0.7108          | 0.8422   | 0.8311 | 0.8825    | 0.7854 |
| 0.1797        | 6.0   | 22848 | 0.8042          | 0.8381   | 0.8314 | 0.8567    | 0.8075 |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
