metadata
author: National Laboratory of the Rockies
license: apache-2.0
language:
- en
base_model:
- google-bert/bert-base-uncased
tags:
- text-classification
- bert
BERT Fine-tuned on PV-Inspection Reports
This model, developed at NLR, is a fine-tuned version of bert-base-uncased on multiple PV inspection reports in the US.
Developed by:
- Jeff Cook, Strategic Energy Analysis Center
- Guilherme Castelão, Strategic Energy Analysis Center
- Danny Chang, Strategic Energy Analysis Center
- Sertac Akar, Accelerated Deployment and Decision Support Center
- James Elsworth, Resilient Infrastructure and Security Center
Model Description
- Base model: bert-base-uncased
- Task: classify type of issue reported
- Training data: PV inspection reports
Intended Uses
Classify the reason(s) of failure from PV installations inspections reports.
How to Use
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("NatLabRockies/pvi-issue-classification")
tokenizer = AutoTokenizer.from_pretrained("NatLabRockies/pvi-issue-classification")
# Example usage
inputs = tokenizer("NEC 110.26 specifies working space clearance about electrical equipment.", return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class_id = logits.argmax().item()
predicted_class = model.config.id2label[predicted_class_id]
print(f"Predicted issue: {predicted_class}")
Training Details
- Training epochs: [number]
- Learning rate: [value]
- Batch size: [value]
Evaluation Results
[Add your metrics: accuracy, F1, etc.]