{"id":24634489,"url":"https://github.com/bjornmelin/nlp-engineering-hub","last_synced_at":"2026-04-17T15:07:17.055Z","repository":{"id":274048692,"uuid":"921747459","full_name":"BjornMelin/nlp-engineering-hub","owner":"BjornMelin","description":"📚 Enterprise NLP systems and LLM applications. 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Features custom language model implementations, efficient inference systems, and production-ready deployment pipelines.\n\n[Features](#features) • [Installation](#installation) • [Quick Start](#quick-start) • [Documentation](#documentation) • [Contributing](#contributing)\n\n## 📑 Table of Contents\n- [Features](#features)\n- [Project Structure](#project-structure)\n- [Prerequisites](#prerequisites)\n- [Installation](#installation)\n- [Quick Start](#quick-start)\n- [Documentation](#documentation)\n  - [Models](#models)\n  - [Pipeline Optimization](#pipeline-optimization)\n  - [Benchmarks](#benchmarks)\n- [Contributing](#contributing)\n- [Versioning](#versioning)\n- [Authors](#authors)\n- [Citation](#citation)\n- [License](#license)\n- [Acknowledgments](#acknowledgments)\n\n## ✨ Features\n- Custom LLM fine-tuning pipelines\n- Multi-GPU distributed training\n- Efficient inference optimization\n- Production deployment patterns\n- Memory-efficient implementations\n\n## 📁 Project Structure\n\n```mermaid\ngraph TD\n    A[nlp-engineering-hub] --\u003e B[models]\n    A --\u003e C[training]\n    A --\u003e D[inference]\n    A --\u003e E[deployment]\n    B --\u003e F[transformers]\n    B --\u003e G[embeddings]\n    C --\u003e H[distributed]\n    C --\u003e I[optimization]\n    D --\u003e J[serving]\n    D --\u003e K[scaling]\n    E --\u003e L[monitoring]\n    E --\u003e M[evaluation]\n```\n\n\u003cdetails\u003e\n\u003csummary\u003eClick to expand full directory structure\u003c/summary\u003e\n\n```plaintext\nnlp-engineering-hub/\n├── models/           # Model implementations\n│   ├── transformers/ # Transformer architectures\n│   └── embeddings/   # Embedding models\n├── training/         # Training utilities\n│   ├── distributed/  # Distributed training\n│   └── optimization/ # Training optimizations\n├── inference/        # Inference optimization\n├── deployment/       # Deployment tools\n├── tests/           # Unit tests\n└── README.md        # Documentation\n```\n\u003c/details\u003e\n\n## 🔧 Prerequisites\n- Python 3.8+\n- CUDA 11.8+\n- Transformers 4.35+\n- PyTorch 2.2+\n- NVIDIA GPU (16GB+ VRAM)\n\n## 📦 Installation\n\n```bash\n# Clone repository\ngit clone https://github.com/BjornMelin/nlp-engineering-hub.git\ncd nlp-engineering-hub\n\n# Create environment\npython -m venv venv\nsource venv/bin/activate\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n## 🚀 Quick Start\n\n```python\nfrom nlp_hub import models, training\n\n# Initialize model\nmodel = models.TransformerWithQuantization(\n    model_name=\"bert-base-uncased\",\n    quantization=\"int8\"\n)\n\n# Configure distributed training\ntrainer = training.DistributedTrainer(\n    model,\n    num_gpus=4,\n    mixed_precision=True\n)\n\n# Train efficiently\ntrainer.train(dataset, batch_size=32)\n```\n\n## 📚 Documentation\n\n### Models\n\n| Model | Task | Performance | Memory Usage |\n|-------|------|-------------|--------------|\n| BERT-Optimized | Classification | 92% accuracy | 2GB |\n| GPT-Efficient | Generation | 85% ROUGE-L | 4GB |\n| T5-Distributed | Translation | 42.5 BLEU | 8GB |\n\n### Pipeline Optimization\n- Automatic mixed precision\n- Dynamic batch sizing\n- Gradient accumulation\n- Model parallelism\n\n### Benchmarks\nPerformance on standard NLP tasks:\n\n| Task | Dataset | Model | GPUs | Training Time | Metric |\n|------|---------|-------|------|---------------|---------|\n| Classification | GLUE | BERT | 4xA100 | 2.5 hours | 92% acc |\n| Generation | CNN/DM | GPT | 8xA100 | 8 hours | 42.3 R1 |\n| QA | SQuAD | T5 | 2xA100 | 4 hours | 88.5 F1 |\n\n## 🤝 Contributing\n- [Contributing Guidelines](CONTRIBUTING.md)\n- [Code of Conduct](CODE_OF_CONDUCT.md)\n- [Development Guide](DEVELOPMENT.md)\n\n## 📌 Versioning\nWe use [SemVer](http://semver.org/) for versioning. For available versions, see the [tags on this repository](https://github.com/BjornMelin/nlp-engineering-hub/tags).\n\n## ✍️ Authors\n**Bjorn Melin**\n- GitHub: [@BjornMelin](https://github.com/BjornMelin)\n- LinkedIn: [Bjorn Melin](https://linkedin.com/in/bjorn-melin)\n\n## 📝 Citation\n```bibtex\n@misc{melin2024nlpengineeringhub,\n  author = {Melin, Bjorn},\n  title = {NLP Engineering Hub: Enterprise Language Model Systems},\n  year = {2024},\n  publisher = {GitHub},\n  url = {https://github.com/BjornMelin/nlp-engineering-hub}\n}\n```\n\n## 📄 License\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## 🙏 Acknowledgments\n- Hugging Face team\n- LangChain developers\n- PyTorch community\n\n---\nMade with 📚 and ❤️ by Bjorn Melin\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbjornmelin%2Fnlp-engineering-hub","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbjornmelin%2Fnlp-engineering-hub","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbjornmelin%2Fnlp-engineering-hub/lists"}