| 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 |