Text Classification
Transformers
PyTorch
distilbert
Network Intrusion Detection
Cybersecurity
Network Packets
text-embeddings-inference
Instructions to use rdpahalavan/bert-network-packet-flow-header-payload with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rdpahalavan/bert-network-packet-flow-header-payload with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rdpahalavan/bert-network-packet-flow-header-payload")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rdpahalavan/bert-network-packet-flow-header-payload") model = AutoModelForSequenceClassification.from_pretrained("rdpahalavan/bert-network-packet-flow-header-payload", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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datasets:
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- rdpahalavan/network-packet-v2
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- rdpahalavan/UNSW-NB15
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- rdpahalavan/CIC-IDS2017
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metrics:
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- accuracy
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- f1
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---
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license: apache-2.0
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datasets:
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- rdpahalavan/UNSW-NB15
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- rdpahalavan/CIC-IDS2017
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- rdpahalavan/network-packet-flow-header-payload
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metrics:
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- accuracy
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- f1
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