code-vs-nl

This model is a fine-tuned version of distilbert-base-uncased on bookcorpus for text and 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