update model card README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,69 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- generated_from_trainer
|
| 4 |
+
metrics:
|
| 5 |
+
- accuracy
|
| 6 |
+
model-index:
|
| 7 |
+
- name: codebert-base-Password_Strength_Classifier
|
| 8 |
+
results: []
|
| 9 |
+
---
|
| 10 |
+
|
| 11 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 12 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 13 |
+
|
| 14 |
+
# codebert-base-Password_Strength_Classifier
|
| 15 |
+
|
| 16 |
+
This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset.
|
| 17 |
+
It achieves the following results on the evaluation set:
|
| 18 |
+
- Loss: 0.0077
|
| 19 |
+
- Accuracy: 0.9975
|
| 20 |
+
- Weighted f1: 0.9975
|
| 21 |
+
- Micro f1: 0.9975
|
| 22 |
+
- Macro f1: 0.9963
|
| 23 |
+
- Weighted recall: 0.9975
|
| 24 |
+
- Micro recall: 0.9975
|
| 25 |
+
- Macro recall: 0.9978
|
| 26 |
+
- Weighted precision: 0.9975
|
| 27 |
+
- Micro precision: 0.9975
|
| 28 |
+
- Macro precision: 0.9948
|
| 29 |
+
|
| 30 |
+
## Model description
|
| 31 |
+
|
| 32 |
+
More information needed
|
| 33 |
+
|
| 34 |
+
## Intended uses & limitations
|
| 35 |
+
|
| 36 |
+
More information needed
|
| 37 |
+
|
| 38 |
+
## Training and evaluation data
|
| 39 |
+
|
| 40 |
+
More information needed
|
| 41 |
+
|
| 42 |
+
## Training procedure
|
| 43 |
+
|
| 44 |
+
### Training hyperparameters
|
| 45 |
+
|
| 46 |
+
The following hyperparameters were used during training:
|
| 47 |
+
- learning_rate: 2e-05
|
| 48 |
+
- train_batch_size: 64
|
| 49 |
+
- eval_batch_size: 64
|
| 50 |
+
- seed: 42
|
| 51 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 52 |
+
- lr_scheduler_type: linear
|
| 53 |
+
- num_epochs: 3
|
| 54 |
+
|
| 55 |
+
### Training results
|
| 56 |
+
|
| 57 |
+
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|
| 58 |
+
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
|
| 59 |
+
| 0.0438 | 1.0 | 8371 | 0.0112 | 0.9956 | 0.9956 | 0.9956 | 0.9935 | 0.9956 | 0.9956 | 0.9963 | 0.9957 | 0.9956 | 0.9908 |
|
| 60 |
+
| 0.0133 | 2.0 | 16742 | 0.0092 | 0.9966 | 0.9967 | 0.9966 | 0.9951 | 0.9966 | 0.9966 | 0.9966 | 0.9967 | 0.9966 | 0.9935 |
|
| 61 |
+
| 0.0067 | 3.0 | 25113 | 0.0077 | 0.9975 | 0.9975 | 0.9975 | 0.9963 | 0.9975 | 0.9975 | 0.9978 | 0.9975 | 0.9975 | 0.9948 |
|
| 62 |
+
|
| 63 |
+
|
| 64 |
+
### Framework versions
|
| 65 |
+
|
| 66 |
+
- Transformers 4.27.4
|
| 67 |
+
- Pytorch 2.0.0
|
| 68 |
+
- Datasets 2.11.0
|
| 69 |
+
- Tokenizers 0.13.3
|