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bert-small-finetuned-eoir_privacy

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bert-small-finetuned-eoir_privacy
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eoir_privacy
metrics:
- accuracy
- f1
model-index:
- name: bert_uncased_L-4_H-512_A-8-finetuned-eoir_privacy
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: eoir_privacy
      type: eoir_privacy
      config: all
      split: train
      args: all
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9175035868005739
    - name: F1
      type: f1
      value: 0.8092868988391376
---

<!-- 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. -->

# bert_uncased_L-4_H-512_A-8-finetuned-eoir_privacy

This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the eoir_privacy dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2159
- Accuracy: 0.9175
- F1: 0.8093

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 63   | 0.2343          | 0.9125   | 0.7953 |
| No log        | 2.0   | 126  | 0.2269          | 0.9110   | 0.8006 |
| No log        | 3.0   | 189  | 0.2159          | 0.9175   | 0.8093 |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1