| --- |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| base_model: microsoft/codebert-base |
| model-index: |
| - name: codebert-base-finetuned-code-ner |
| 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. --> |
|
|
| # codebert-base-finetuned-code-ner |
|
|
| This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3522 |
| - Precision: 0.6297 |
| - Recall: 0.6417 |
| - F1: 0.6356 |
| - Accuracy: 0.9185 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 191 | 0.4601 | 0.4861 | 0.4578 | 0.4715 | 0.8853 | |
| | No log | 2.0 | 382 | 0.3989 | 0.5806 | 0.5243 | 0.5510 | 0.8996 | |
| | 0.5081 | 3.0 | 573 | 0.3547 | 0.5723 | 0.6017 | 0.5866 | 0.9059 | |
| | 0.5081 | 4.0 | 764 | 0.3507 | 0.6161 | 0.6115 | 0.6138 | 0.9135 | |
| | 0.5081 | 5.0 | 955 | 0.3412 | 0.6299 | 0.6252 | 0.6276 | 0.9161 | |
| | 0.2299 | 6.0 | 1146 | 0.3418 | 0.6162 | 0.6465 | 0.6310 | 0.9175 | |
| | 0.2299 | 7.0 | 1337 | 0.3497 | 0.6288 | 0.6287 | 0.6287 | 0.9175 | |
| | 0.1618 | 8.0 | 1528 | 0.3474 | 0.6340 | 0.6397 | 0.6368 | 0.9189 | |
| | 0.1618 | 9.0 | 1719 | 0.3501 | 0.6262 | 0.6432 | 0.6346 | 0.9179 | |
| | 0.1618 | 10.0 | 1910 | 0.3522 | 0.6297 | 0.6417 | 0.6356 | 0.9185 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.23.1 |
| - Pytorch 1.12.1+cu113 |
| - Datasets 2.6.1 |
| - Tokenizers 0.13.1 |
|
|
