<!-- 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 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
