Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("DunnBC22/codebert-base-mlm-Malicious_URLs")
model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/codebert-base-mlm-Malicious_URLs")Quick Links
codebert-base-mlm-Malicious_URLs
This model is a fine-tuned version of microsoft/codebert-base-mlm on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7442
- Accuracy: 0.7322
- Weighted f1: 0.6538
- Micro f1: 0.7322
- Macro f1: 0.4303
- Weighted recall: 0.7322
- Micro recall: 0.7322
- Macro recall: 0.4233
- Weighted precision: 0.6314
- Micro precision: 0.7322
- Macro precision: 0.6034
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: 42
- 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: 2
Training results
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
License Notice
This model is a fine-tuned derivative of a pretrained model. Users must comply with the original model license.
Dataset Notice
This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/codebert-base-mlm-Malicious_URLs")