README.md

---
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
```python
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.]