metadata
language: en
license: mit
library_name: transformers
pipeline_tag: token-classification
tags:
- deberta
- ner
- token-classification
- cybersecurity
- logs
base_model: microsoft/deberta-v3-base
DeBERTa-v3 Log Entity Recognition Model
Fine-tuned DeBERTa-v3-small for Named Entity Recognition on system and cloud logs.
Model Details
- Base Model: microsoft/deberta-v3-base
- Training Data: 7003 synthetic + real logs
- Validation F1: Check evaluation_results.txt
Entities
['O', 'B-SERVICE', 'I-SERVICE', 'B-ERROR', 'I-ERROR', 'B-HOST', 'I-HOST', 'B-PROCESS', 'I-PROCESS']
Usage
from transformers import AutoTokenizer, AutoModelForTokenClassification
from peft import PeftModel
model_id = "YOUR_USERNAME/log-ner-deberta-lora"
tokenizer = AutoTokenizer.from_pretrained(model_id)
base_model = AutoModelForTokenClassification.from_pretrained("microsoft/deberta-v3-base")
model = PeftModel.from_pretrained(base_model, model_id)
# Extract entities
text = "nginx timeout on server1"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
Training Configuration
- LoRA rank: 32
- Training epochs: 15
- Learning rate: 0.0003