from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("your-username/infrnce-bert-log-classifier")
model = AutoModelForSequenceClassification.from_pretrained("your-username/infrnce-bert-log-classifier")

# Example usage
log_text = "Your OpenStack log entry here"
inputs = tokenizer(log_text, return_tensors="pt", truncation=True, padding=True, max_length=512)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class_id = predictions.argmax().item()

print(f"Predicted class: {model.config.id2label[predicted_class_id]}")

Training Data

The model was trained on a curated dataset of OpenStack Nova logs with both regex-based classifications and semantic clustering.

Performance

The model was trained with controlled accuracy to achieve optimal performance on log classification tasks.

kxshrx/infrnce-bert-classifier

INFRNCE BERT Log Classification Model

Model Details

Labels

Usage

Training Data

Performance

kxshrx/
infrnce-bert-classifier