---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- bookcorpus
- codeparrot/github-code
metrics:
- accuracy
- f1
base_model: distilbert-base-uncased
model-index:
- name: code-vs-nl
  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. -->

# code-vs-nl

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 
on [bookcorpus](https://huggingface.co/datasets/bookcorpus) for text and [codeparrot/github-code](https://huggingface.co/datasets/codeparrot/github-code) for code datasets.
It achieves the following results on the evaluation set:
- Loss: 0.5180
- Accuracy: 0.9951
- F1 Score: 0.9950

## Model description

As it's a finetuned model, it's architecture is same as distilbert-base-uncased for Sequence Classification

## Intended uses & limitations

Can be used to classify documents into text and code

## Training and evaluation data

It is a mix of above two datasets, equally random sampled

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-07
- train_batch_size: 256
- eval_batch_size: 1024
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.5732        | 0.07  | 500  | 0.5658          | 0.9934   | 0.9934   |
| 0.5254        | 0.14  | 1000 | 0.5180          | 0.9951   | 0.9950   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2