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---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
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 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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