File size: 1,998 Bytes
f570857 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
---
license: bsd-3-clause
base_model: Salesforce/codet5p-770m
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: Salesforce-codet5p-770m-finetuned-defect-cwe-group
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. -->
# Salesforce-codet5p-770m-finetuned-defect-cwe-group
This model is a fine-tuned version of [Salesforce/codet5p-770m](https://huggingface.co/Salesforce/codet5p-770m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5725
- Accuracy: 0.7760
- Precision: 0.6093
- Recall: 0.5124
## 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: 8
- eval_batch_size: 8
- seed: 4711
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| No log | 1.0 | 462 | 0.6359 | 0.7124 | 0.5669 | 0.4538 |
| 0.7472 | 2.0 | 925 | 0.5540 | 0.7496 | 0.6085 | 0.4692 |
| 0.5458 | 3.0 | 1387 | 0.5520 | 0.7490 | 0.5918 | 0.5082 |
| 0.4192 | 4.0 | 1850 | 0.5505 | 0.7558 | 0.5904 | 0.5231 |
| 0.3356 | 4.99 | 2310 | 0.5725 | 0.7760 | 0.6093 | 0.5124 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|