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
File size: 617 Bytes
93af2de | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"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
} |