---
language:
- en
license: mit
base_model: prajjwal1/bert-tiny
tags:
- pytorch
- BertForTokenClassification
- bert-tiny
- generated_from_trainer
- named-entity-recognition
model-index:
- name: bert-tiny-privacy
  results: []
datasets:
- beki/privy
library_name: transformers
pipeline_tag: token-classification
---

<!-- 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-tiny-privacy

This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the beki/privy dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0235

## Model description

This model can be used to detect personal information traces from JSON, SQL, HTML and XML and can be used as a model for redacting such information.

## 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: 4e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 13434865
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 15000

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1891        | 0.19  | 2500  | 0.1369          |
| 0.0869        | 0.38  | 5000  | 0.0503          |
| 0.0609        | 0.57  | 7500  | 0.0314          |
| 0.0512        | 0.76  | 10000 | 0.0259          |
| 0.0493        | 0.95  | 12500 | 0.0240          |
| 0.048         | 1.14  | 15000 | 0.0237          |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0