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