# profile.yaml
name: Ali Nawaz
role: Python Developer Β· ML & NLP Engineer
status: open to opportunities
strengths:
- fine-tuning and evaluating transformer models
- shipping models as SDKs, REST APIs and demos
- secure, tested backends (Django, FastAPI)
how_i_work: [reproducible runs, baselines first, honest reporting]
shipped: [PyPI package, Hugging Face models and Spaces, Dockerized apps, CI pipelines]I'm a Python developer who takes machine learning work from a research notebook to something other people can install, call and trust. I build the whole chain: labeled datasets, training and evaluation, then packaging, APIs and deployment. My NLP projects focus on low-resource languages, which taught me to benchmark carefully and report negative results when the numbers demand it.
| Fine-tuned XLM-RoBERTa and classical TF-IDF + LinearSVC models, trained tokenizers, and benchmarked against 14 baselines. Seeded, logged runs so results can be reproduced. | FastAPI and Django services, including a zero-knowledge document platform with AES-256-GCM encryption, single-use QR passes and rate limiting. | Docker, GitHub Actions CI, automated tests, a PyPI package, Hugging Face releases and documentation sites, so work ships and stays maintainable. |
|
Transformer sentiment analysis with SDK and API
Sentiment analysis (Sindhi) with a fine-tuned XLM-RoBERTa model, a Python SDK, and a FastAPI service. Every run is seeded and logs its config, metrics, and data hashes. |
Classical ML classifier and labeled dataset
Dual TF-IDF + LinearSVC classifier trained on a fully human-labeled 4,420-sentence dataset: 91.7% accuracy and 0.918 macro F1, with a transparent writeup of why accuracy dropped from v1. |
|
Zero-knowledge document sharing
High-security document sharing with AES-256-GCM encryption, single-use QR passes, rate-limiting defense, granular expiration controls, and zero server-side plaintext exposure. |
Corpus-driven subword tokenizer
SentencePiece Unigram tokenizer trained on 1.46M+ sentences and benchmarked against 14 baseline tokenizers, with a 0.000% normalized-length loss round-trip check. |
Latest commits, pull requests and releases
- π Published release Sindhi Tokenizer v1.0.0 β First Stable Release in alinawazcode/sindhi-tokenizer
- π Merged PR #15 in alinawazcode/sindhi-nlp
- πͺ Opened PR #15 in alinawazcode/sindhi-nlp
- π Merged PR #14 in alinawazcode/sindhi-nlp
- πͺ Opened PR #14 in alinawazcode/sindhi-nlp
- β Closed PR #153 in abhiksark/pythonlings
- π Merged PR #24 in alinawazcode/passli
- πͺ Opened PR #24 in alinawazcode/passli
- π Merged PR #23 in alinawazcode/passli
- πͺ Opened PR #23 in alinawazcode/passli
Updated automatically every 6 hours by GitHub Actions.


