Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use b1nch3f/owasp-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use b1nch3f/owasp-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="b1nch3f/owasp-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("b1nch3f/owasp-bert") model = AutoModelForSequenceClassification.from_pretrained("b1nch3f/owasp-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
owasp-bert
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2889
- Accuracy: 0.902
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 125 | 0.6790 | 0.699 |
| No log | 2.0 | 250 | 0.5486 | 0.712 |
| No log | 3.0 | 375 | 0.3549 | 0.876 |
| 0.5869 | 4.0 | 500 | 0.4027 | 0.825 |
| 0.5869 | 5.0 | 625 | 0.2889 | 0.902 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for b1nch3f/owasp-bert
Base model
google-bert/bert-base-uncased