---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: NetFID-NetFlow
  results: []
---

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# NetFID-NetFlow

This model is a train-from-scratch version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on a mixed-source NetFlow dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7583
- Accuracy: 0.7759

## Model description
Pretrained model with [bert-base-uncased](https://huggingface.co/bert-base-uncased) (110M parameters) as the base architecture.

## Intended uses & limitations
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.

## How to use
The usage is almost the same as regular BERT models, except that the input data is NetFlow data.


## Training and evaluation data
TBD.

## Training procedure
TBD.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3