Instructions to use eng-ong/ft-bert-base-uncased-for-binary-search with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eng-ong/ft-bert-base-uncased-for-binary-search with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eng-ong/ft-bert-base-uncased-for-binary-search")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eng-ong/ft-bert-base-uncased-for-binary-search") model = AutoModelForSequenceClassification.from_pretrained("eng-ong/ft-bert-base-uncased-for-binary-search") - Notebooks
- Google Colab
- Kaggle
ft-bert-base-uncased-for-binary-search
This model is a fine-tuned version of bert-base-uncased on the https://www.kaggle.com/datasets/skywardai/network-vulnerability dataset. It achieves the following results on the evaluation set:
- Loss: 0.0003
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0107 | 1.0 | 50 | 0.0047 |
| 0.0014 | 2.0 | 100 | 0.0009 |
| 0.0008 | 3.0 | 150 | 0.0006 |
| 0.0007 | 4.0 | 200 | 0.0004 |
| 0.0005 | 5.0 | 250 | 0.0003 |
| 0.0005 | 6.0 | 300 | 0.0003 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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Model tree for eng-ong/ft-bert-base-uncased-for-binary-search
Base model
google-bert/bert-base-uncased