--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: cybersecurity-ner results: [] datasets: - bnsapa/cybersecurity-ner language: - en library_name: transformers widget: - text: "microsoft and google are working to build AI models" - text: "Having obtained the necessary permissions from the user, Riltok contacts its C&C server." - text: "Tweets in Twitter can be controversial" - text: "xyz is custom virus gains access to the messages in the victim's mobile and contacts the attacker's server" --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cybersecurity-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [cybersecurity-ner](https://huggingface.co/datasets/bnsapa/cybersecurity-ner) dataset. It achieves the following results on the evaluation set: - Loss: 0.2196 - Precision: 0.7942 - Recall: 0.7925 - F1: 0.7933 - Accuracy: 0.9508 ## 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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 167 | 0.2492 | 0.6870 | 0.7406 | 0.7128 | 0.9293 | | No log | 2.0 | 334 | 0.2026 | 0.7733 | 0.7346 | 0.7534 | 0.9420 | | 0.2118 | 3.0 | 501 | 0.1895 | 0.7735 | 0.7934 | 0.7833 | 0.9493 | | 0.2118 | 4.0 | 668 | 0.1834 | 0.7785 | 0.8189 | 0.7982 | 0.9511 | | 0.2118 | 5.0 | 835 | 0.2060 | 0.8113 | 0.7965 | 0.8039 | 0.9522 | | 0.0507 | 6.0 | 1002 | 0.2153 | 0.7692 | 0.8226 | 0.7950 | 0.9511 | | 0.0507 | 7.0 | 1169 | 0.2141 | 0.7866 | 0.7962 | 0.7914 | 0.9507 | | 0.0507 | 8.0 | 1336 | 0.2196 | 0.7942 | 0.7925 | 0.7933 | 0.9508 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0