wandb/sourcecode-detection

1---2library_name: transformers3base_model: huggingface/CodeBERTa-small-v14tags:5- generated_from_trainer6metrics:7- f18- accuracy9- precision10- recall11model-index:12- name: CodeBERTa-small-v1-sourcecode-detection-clf13  results: []14---15 16<!-- This model card has been generated automatically according to the information the Trainer had access to. You17should probably proofread and complete it, then remove this comment. -->18 19# CodeBERTa-small-v1-sourcecode-detection-clf20 21This model is a fine-tuned version of [huggingface/CodeBERTa-small-v1](https://huggingface.co/huggingface/CodeBERTa-small-v1) on an unknown dataset.22It achieves the following results on the evaluation set:23- Loss: 0.017124- F1: 0.997525- Accuracy: 0.997526- Precision: 0.997527- Recall: 0.997528 29## Model description30 31More information needed32 33## Intended uses & limitations34 35More information needed36 37## Training and evaluation data38 39More information needed40 41## Training procedure42 43### Training hyperparameters44 45The following hyperparameters were used during training:46- learning_rate: 0.000347- train_batch_size: 32048- eval_batch_size: 32049- seed: 202450- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments51- lr_scheduler_type: cosine52- lr_scheduler_warmup_ratio: 0.153- num_epochs: 154 55### Training results56 57| Training Loss | Epoch  | Step | Validation Loss | F1     | Accuracy | Precision | Recall |58|:-------------:|:------:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|59| No log        | 0      | 0    | 0.6981          | 0.3337 | 0.5001   | 0.6162    | 0.5001 |60| 0.0294        | 0.1420 | 1000 | 0.0398          | 0.9947 | 0.9947   | 0.9947    | 0.9947 |61| 0.0076        | 0.2841 | 2000 | 0.0211          | 0.9968 | 0.9968   | 0.9968    | 0.9968 |62| 0.0053        | 0.4261 | 3000 | 0.0188          | 0.9973 | 0.9973   | 0.9973    | 0.9973 |63| 0.0056        | 0.5681 | 4000 | 0.0166          | 0.9976 | 0.9976   | 0.9976    | 0.9976 |64| 0.0044        | 0.7101 | 5000 | 0.0172          | 0.9975 | 0.9975   | 0.9975    | 0.9975 |65| 0.0009        | 0.8522 | 6000 | 0.0171          | 0.9975 | 0.9975   | 0.9975    | 0.9975 |66| 0.0052        | 0.9942 | 7000 | 0.0171          | 0.9975 | 0.9975   | 0.9975    | 0.9975 |67 68 69### Framework versions70 71- Transformers 4.46.372- Pytorch 2.5.173- Datasets 3.1.074- Tokenizers 0.20.375