thiomajid/codebert-java-inconsistency

1---2library_name: transformers3base_model: microsoft/codebert-base4tags:5- generated_from_trainer6metrics:7- accuracy8- f19- precision10- recall11model-index:12- name: codebert-java-inconsistency13  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# codebert-java-inconsistency20 21This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset.22It achieves the following results on the evaluation set:23- Loss: 0.354324- Accuracy: 0.916725- F1: 0.918326- Precision: 0.923527- Recall: 0.916728 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: 2e-0547- train_batch_size: 3248- eval_batch_size: 3249- seed: 4250- gradient_accumulation_steps: 251- total_train_batch_size: 6452- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments53- lr_scheduler_type: cosine54- num_epochs: 3055- mixed_precision_training: Native AMP56 57### Training results58 59| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |60|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|61| 1.4625        | 3.1290  | 50   | 0.8954          | 0.7531   | 0.7554 | 0.7765    | 0.7531 |62| 0.5834        | 6.2581  | 100  | 0.5559          | 0.8189   | 0.8241 | 0.8483    | 0.8189 |63| 0.2858        | 9.3871  | 150  | 0.4046          | 0.8930   | 0.8945 | 0.8995    | 0.8930 |64| 0.1624        | 12.5161 | 200  | 0.4461          | 0.8642   | 0.8661 | 0.8750    | 0.8642 |65| 0.1084        | 15.6452 | 250  | 0.4012          | 0.9012   | 0.9038 | 0.9123    | 0.9012 |66| 0.074         | 18.7742 | 300  | 0.4689          | 0.8765   | 0.8817 | 0.8972    | 0.8765 |67| 0.0574        | 21.9032 | 350  | 0.4885          | 0.8807   | 0.8845 | 0.8970    | 0.8807 |68| 0.0452        | 25.0    | 400  | 0.4900          | 0.8848   | 0.8888 | 0.9011    | 0.8848 |69| 0.0396        | 28.1290 | 450  | 0.4896          | 0.8765   | 0.8805 | 0.8934    | 0.8765 |70 71 72### Framework versions73 74- Transformers 4.51.375- Pytorch 2.6.0+cu12476- Datasets 3.5.077- Tokenizers 0.21.178