File size: 1,796 Bytes
080fcfd 689598d 080fcfd 689598d 080fcfd 689598d 080fcfd 689598d 080fcfd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
---
license: apache-2.0
language: en
library_name: transformers
pipeline_tag: text-classification
tags:
- text-classification
- distilbert
- log-analysis
- openstack
- fine-tuned
widget:
- text: "Instance 1234 has failed to connect to the network"
---
# INFRNCE BERT Log Classification Model
This is a fine-tuned DistilBERT model for classifying OpenStack Nova log entries into different operational categories.
## Model Details
- **Base Model**: distilbert-base-uncased
- **Task**: Multi-class text classification
- **Number of Labels**: 6
- **Domain**: OpenStack log analysis
## Labels
The model classifies logs into the following categories:
- Error_Handling, - Instance_Management, - Network_Operations, - Resource_Management, - Scheduler_Operations, - System_Operations
## Usage
```python
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.
|