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# ComNumPNdistilBERT

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a subset (800 samples as training and 200 samples as validation) of the This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a subset (800 samples as training and 200 samples as validation) of the [ComNum](https://huggingface.co/datasets/abbassix/ComNum) dataset. We just separated a numeral into digits as a reframing before tokenizing them. For example '1871' becomes '1 8 7 1'.
It achieves the following results on the evaluation subset:
- Loss: 0.3672
- Accuracy: 0.75

It achieves the following results on the test set:
- Loss: 0.3833
- Accuracy: 0.7413
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## 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: 5e-05
- 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
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 100  | 0.5905          | 0.69     |
| No log        | 2.0   | 200  | 0.3452          | 0.77     |
| No log        | 3.0   | 300  | 0.3672          | 0.75     |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "ComNumPNdistilBERT", "results": []}]}
transformers tensorboard safetensors distilbert text-classification generated_from_trainer base_model:distilbert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us