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
Download special_tokens_map.json from rdpahalavan/bert-network-packet-flow-header-payload: direct link, hf CLI and curl.
- Browser
- Download file 125 Bytes
-
https://huggingface.co/rdpahalavan/bert-network-packet-flow-header-payload/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://rdpahalavan/bert-network-packet-flow-header-payload/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/rdpahalavan/bert-network-packet-flow-header-payload/resolve/main/special_tokens_map.json
125 Bytes
| { | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "unk_token": "[UNK]" | |
| } | |