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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: NetFID-NetFlow |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# NetFID-NetFlow |
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This model is a train-from-scratch version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on a mixed-source NetFlow dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7583 |
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- Accuracy: 0.7759 |
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## Model description |
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Pretrained model with [bert-base-uncased](https://huggingface.co/bert-base-uncased) (110M parameters) as the base architecture. |
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## Intended uses & limitations |
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This model is mainly used to get embeddings for NetFlow data, which can be further used for ML-based tasks e.g., classification, clustering, etc. |
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## How to use |
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The usage is almost the same as regular BERT models, except that the input data is NetFlow data. |
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## Training and evaluation data |
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TBD. |
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## Training procedure |
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TBD. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.31.0.dev0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.0 |
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- Tokenizers 0.13.3 |
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