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---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
base_model: microsoft/codebert-base
model-index:
- name: codebert-base-finetuned-code-ner-15e
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-15e
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: 0.3831
- Precision: 0.6363
- Recall: 0.6494
- F1: 0.6428
- Accuracy: 0.9197
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 191 | 0.4566 | 0.5021 | 0.4220 | 0.4585 | 0.8827 |
| No log | 2.0 | 382 | 0.3756 | 0.5699 | 0.5764 | 0.5731 | 0.9043 |
| 0.5133 | 3.0 | 573 | 0.3605 | 0.6001 | 0.5767 | 0.5882 | 0.9093 |
| 0.5133 | 4.0 | 764 | 0.3500 | 0.6130 | 0.6130 | 0.6130 | 0.9153 |
| 0.5133 | 5.0 | 955 | 0.3501 | 0.6337 | 0.6172 | 0.6254 | 0.9178 |
| 0.2203 | 6.0 | 1146 | 0.3645 | 0.6250 | 0.6352 | 0.6300 | 0.9163 |
| 0.2203 | 7.0 | 1337 | 0.3488 | 0.6263 | 0.6422 | 0.6341 | 0.9189 |
| 0.1457 | 8.0 | 1528 | 0.3575 | 0.6372 | 0.6397 | 0.6384 | 0.9194 |
| 0.1457 | 9.0 | 1719 | 0.3662 | 0.6406 | 0.6343 | 0.6375 | 0.9189 |
| 0.1457 | 10.0 | 1910 | 0.3613 | 0.6374 | 0.6473 | 0.6423 | 0.9201 |
| 0.107 | 11.0 | 2101 | 0.3716 | 0.6329 | 0.6544 | 0.6435 | 0.9197 |
| 0.107 | 12.0 | 2292 | 0.3754 | 0.6328 | 0.6487 | 0.6406 | 0.9193 |
| 0.107 | 13.0 | 2483 | 0.3826 | 0.6395 | 0.6490 | 0.6443 | 0.9204 |
| 0.0863 | 14.0 | 2674 | 0.3821 | 0.6368 | 0.6535 | 0.6451 | 0.9200 |
| 0.0863 | 15.0 | 2865 | 0.3831 | 0.6363 | 0.6494 | 0.6428 | 0.9197 |
### Evaluation results
| | Algorithm | Application | Class | Code_Block | Data_Structure | Data_Type | Device | Error_Name | File_Name | File_Type | Function | HTML_XML_Tag | Keyboard_IP | Language | Library | Operating_System | Output_Block | User_Interface_Element | User_Name | Value | Variable | Version | Website | overall_precision | overall_recall | overall_f1 | overall_accuracy |
|:----------|------------:|--------------:|------------:|-------------:|-----------------:|------------:|----------:|-------------:|------------:|------------:|-----------:|---------------:|--------------:|-----------:|-----------:|-------------------:|---------------:|-------------------------:|------------:|-----------:|-----------:|-----------:|----------:|--------------------:|-----------------:|-------------:|-------------------:|
| precision | 0 | 0.619835 | 0.680851 | 0.455629 | 0.813187 | 0.592593 | 0.395062 | 0.181818 | 0.800505 | 0.775956 | 0.757664 | 0.585366 | 0.333333 | 0.689769 | 0.61807 | 0.769231 | 0.0212766 | 0.542214 | 0.4375 | 0.370236 | 0.560479 | 0.883721 | 0.382353 | 0.626308 | 0.642171 | 0.63414 | 0.918927 |
| recall | 0 | 0.677711 | 0.696864 | 0.494253 | 0.840909 | 0.8 | 0.533333 | 0.333333 | 0.794486 | 0.628319 | 0.631387 | 0.470588 | 0.0169492 | 0.81323 | 0.546279 | 0.843373 | 0.04 | 0.653846 | 0.518519 | 0.52987 | 0.54482 | 0.914089 | 0.270833 | 0.626308 | 0.642171 | 0.63414 | 0.918927 |
| f1 | 0 | 0.647482 | 0.688765 | 0.474156 | 0.826816 | 0.680851 | 0.453901 | 0.235294 | 0.797484 | 0.694377 | 0.688786 | 0.521739 | 0.0322581 | 0.746429 | 0.579961 | 0.804598 | 0.0277778 | 0.592821 | 0.474576 | 0.435897 | 0.552538 | 0.898649 | 0.317073 | 0.626308 | 0.642171 | 0.63414 | 0.918927 |
| number | 31 | 664 | 1148 | 696 | 264 | 120 | 60 | 30 | 798 | 226 | 822 | 102 | 59 | 257 | 551 | 83 | 25 | 442 | 54 | 385 | 859 | 291 | 48 | 0.626308 | 0.642171 | 0.63414 | 0.918927 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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