---
language: en
tags:
- text-classification
- requirements-engineering
- bert
datasets:
- promise-nfr
metrics:
- accuracy
- f1
model-index:
- name: RequirementClassifier
  results:
  - task:
      type: text-classification
      name: Requirement Classification
    dataset:
      name: PROMISE NFR
      type: promise-nfr
    metrics:
    - type: accuracy
      name: Accuracy
      value: 0.0
---

# RequirementClassifier

Version: 27

## Model Description

This model is a fine-tuned BERT model for binary classification of software requirements.
It classifies text as either "requirement" or "non-requirement".

## Intended Uses

- Classify software requirement documents
- Identify requirement vs non-requirement statements
- Automated requirement extraction from documents

## Training Data

The model was trained on the PROMISE NFR dataset with additional non-requirement examples.

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("rajinikarcg/RequirementClassifier")
model = AutoModelForSequenceClassification.from_pretrained("rajinikarcg/RequirementClassifier")

# Prepare input
text = "The system shall respond within 2 seconds"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

# Get prediction
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    prediction = torch.argmax(logits, dim=-1).item()

# Map to label
labels = ["non-requirement", "requirement"]
print(f"Prediction: {labels[prediction]}")
```

## Version History

- 27: Latest version

## Citation

If you use this model, please cite the PROMISE NFR dataset.