Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use DunnBC22/codebert-base-Password_Strength_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/codebert-base-Password_Strength_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/codebert-base-Password_Strength_Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/codebert-base-Password_Strength_Classifier") model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/codebert-base-Password_Strength_Classifier") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_Macro F1": 0.9962698425655323, | |
| "eval_Macro Precision": 0.9947514976785458, | |
| "eval_Macro Recall": 0.9978107425679793, | |
| "eval_Micro F1": 0.9974686757963591, | |
| "eval_Micro Precision": 0.9974686757963591, | |
| "eval_Micro Recall": 0.9974686757963591, | |
| "eval_Weighted F1": 0.9974744654630983, | |
| "eval_Weighted Precision": 0.9974905219201232, | |
| "eval_Weighted Recall": 0.9974686757963591, | |
| "eval_accuracy": 0.9974686757963591, | |
| "eval_loss": 0.007663697935640812, | |
| "eval_runtime": 4112.3353, | |
| "eval_samples_per_second": 32.566, | |
| "eval_steps_per_second": 0.509, | |
| "train_loss": 0.021318447113758642, | |
| "train_runtime": 180269.7266, | |
| "train_samples_per_second": 8.915, | |
| "train_steps_per_second": 0.139 | |
| } |