---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cuad
model-index:
- name: tiny-bert-finetuned-cuad
  results: []
---

<!-- 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. -->

# tiny-bert-finetuned-cuad

This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the portion of cuad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4606

# Note 
The model was not trained on the whole dataset but, the first 10% of `train` + the first 10% of `test`.
```bash
raw_datasets_train, raw_datasets_test = load_dataset("cuad", split=['train[:10%]', 'test[:10%]']) 
datasets = DatasetDict({'train': raw_datasets_train, 'validation': raw_datasets_test})
```

## 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: 1024
- eval_batch_size: 1024
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 136  | 2.9644          |
| No log        | 2.0   | 272  | 1.9337          |
| No log        | 3.0   | 408  | 1.4375          |
| 2.7124        | 4.0   | 544  | 1.0978          |
| 2.7124        | 5.0   | 680  | 0.8571          |
| 2.7124        | 6.0   | 816  | 0.6907          |
| 2.7124        | 7.0   | 952  | 0.5799          |
| 0.9512        | 8.0   | 1088 | 0.5105          |
| 0.9512        | 9.0   | 1224 | 0.4726          |
| 0.9512        | 10.0  | 1360 | 0.4606          |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1