| --- |
| license: mit |
| language: |
| - en |
| library_name: transformers |
| tags: |
| - text-classification |
| - naics |
| - industry-classification |
| - github |
| - roberta |
| datasets: |
| - custom |
| metrics: |
| - f1 |
| - accuracy |
| pipeline_tag: text-classification |
| --- |
| |
| # NAICS GitHub Repository Classifier |
|
|
| A fine-tuned RoBERTa-large model that classifies GitHub repositories into **19 NAICS (North American Industry Classification System)** industry sectors based on repository metadata. |
|
|
| ## Model Description |
|
|
| This model takes GitHub repository information (name, description, topics, README) and predicts the most likely industry sector the repository belongs to. |
|
|
| - **Model:** `roberta-large` (355M parameters) |
| - **Task:** Multi-class text classification (19 classes) |
| - **Language:** English |
| - **Training Data:** 6,588 labeled GitHub repositories |
|
|
| ## Intended Use |
|
|
| - Classifying GitHub repositories by industry sector |
| - Analyzing open-source software ecosystem by industry |
| - Research on technology adoption across industries |
|
|
| ## NAICS Classes |
|
|
| | Label | NAICS Code | Industry Sector | |
| |-------|------------|-----------------| |
| | 0 | 11 | Agriculture, Forestry, Fishing and Hunting | |
| | 1 | 21 | Mining, Quarrying, Oil and Gas Extraction | |
| | 2 | 22 | Utilities | |
| | 3 | 23 | Construction | |
| | 4 | 31-33 | Manufacturing | |
| | 5 | 42 | Wholesale Trade | |
| | 6 | 44-45 | Retail Trade | |
| | 7 | 48-49 | Transportation and Warehousing | |
| | 8 | 51 | Information | |
| | 9 | 52 | Finance and Insurance | |
| | 10 | 53 | Real Estate and Rental | |
| | 11 | 54 | Professional, Scientific, Technical Services | |
| | 12 | 56 | Administrative and Support Services | |
| | 13 | 61 | Educational Services | |
| | 14 | 62 | Health Care and Social Assistance | |
| | 15 | 71 | Arts, Entertainment, and Recreation | |
| | 16 | 72 | Accommodation and Food Services | |
| | 17 | 81 | Other Services | |
| | 18 | 92 | Public Administration | |
|
|
| ## Usage |
|
|
| ### Quick Start |
|
|
| ```python |
| from transformers import pipeline |
| |
| classifier = pipeline( |
| "text-classification", |
| model="alexanderquispe/naics-github-classifier" |
| ) |
| |
| text = "Repository: bank-api | Description: REST API for banking transactions | README: A secure API for financial operations" |
| result = classifier(text) |
| print(result) |
| # [{'label': '52', 'score': 0.95}] # Finance and Insurance |
| ``` |
|
|
| ### Full Example |
|
|
| ```python |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer |
| import torch |
| |
| model = AutoModelForSequenceClassification.from_pretrained("alexanderquispe/naics-github-classifier") |
| tokenizer = AutoTokenizer.from_pretrained("alexanderquispe/naics-github-classifier") |
| |
| # Format input |
| text = "Repository: mediscan | Description: AI diagnostic tool for radiology | Topics: healthcare; medical-imaging; deep-learning | README: MediScan uses computer vision to assist radiologists..." |
| |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512) |
| outputs = model(**inputs) |
| predicted_class = torch.argmax(outputs.logits, dim=1).item() |
| |
| # Map to NAICS code |
| id2label = model.config.id2label |
| print(f"Predicted NAICS: {id2label[predicted_class]}") # 62 (Health Care) |
| ``` |
|
|
| ## Input Format |
|
|
| The model expects text in this format: |
|
|
| ``` |
| Repository: {repo_name} | Description: {description} | Topics: {topics} | README: {readme_content} |
| ``` |
|
|
| | Field | Required | Description | |
| |-------|----------|-------------| |
| | Repository | Yes | Repository name | |
| | Description | No | Short description | |
| | Topics | No | Semicolon-separated tags | |
| | README | No | README content (can be truncated) | |
|
|
| ## Training Details |
|
|
| ### Training Data |
|
|
| - **Source:** GitHub repositories labeled with NAICS codes |
| - **Size:** 6,588 examples |
| - **Classes:** 19 NAICS sectors |
| - **Split:** 70% train / 10% validation / 20% test |
|
|
| ### Training Hyperparameters |
|
|
| | Parameter | Value | |
| |-----------|-------| |
| | Base Model | `roberta-large` | |
| | Batch Size | 32 | |
| | Learning Rate | 2e-5 | |
| | Epochs | 8 | |
| | Max Sequence Length | 512 | |
| | Optimizer | AdamW | |
| | Weight Decay | 0.01 | |
| | Early Stopping Patience | 5 | |
|
|
| ### Preprocessing |
|
|
| Text preprocessing includes: |
| - Removal of markdown badges and formatting |
| - URL cleaning (keep domain names) |
| - License header removal |
| - Code block removal (keep language indicators) |
| - Technology term normalization (js → javascript, py → python) |
| - Whitespace normalization |
|
|
| ## Limitations |
|
|
| - Trained primarily on English repositories |
| - May not generalize to non-software repositories |
| - NAICS code 55 (Management of Companies) excluded due to limited training data |
| - Performance may vary for repositories with minimal README content |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{naics-github-classifier, |
| author = {{GitHub, Inc.} and Xu, Kevin and Quispe, Alexander}, |
| title = {NAICS GitHub Repository Classifier}, |
| year = {2025}, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/alexanderquispe/naics-github-classifier} |
| } |
| ``` |
|
|
| ## Repository |
|
|
| Training code and data preparation: [github.com/alexanderquispe/naics-github-train](https://github.com/alexanderquispe/naics-github-train) |
|
|
