SOTA Models
Llama, Claude, Gemini, GPT-4 — modern landscape।
GitHub এ ১০০M+ developer। Recruiter রা দেখে CV না — GitHub profile। আপনার যদি একটা popular open-source NLP project থাকে, FAANG থেকে DM আসবে। Open source = best resume।
Open Source Contribution মানে অন্যের project এ code/doc/test contribute করা, অথবা নিজের project public করা। NLP ecosystem এর backbone হলো open source — Hugging Face Transformers, spaCy, NLTK, LangChain সব community-driven। Contribute করলে: skill বাড়ে, network grow করে, এবং career-changing opportunity আসে।
Open source হলো একটা global classroom। আপনি যখন PR (Pull Request) পাঠান, world-class engineer রা আপনার code review করে — free mentorship। শুরু সহজ: typo fix, doc improvement, test add। ধীরে ধীরে feature, bug fix, এবং নিজের library তে পৌঁছাবেন।
# Publish your own NLP package to PyPI
# Project structure:
# my_nlp_tool/
# ├── pyproject.toml
# ├── README.md
# ├── LICENSE
# └── my_nlp_tool/
# ├── __init__.py
# └── analyzer.py
# pyproject.toml
"""
[project]
name = "bangla-nlp-toolkit"
version = "0.1.0"
description = "Bengali NLP utilities — tokenizer, stemmer, sentiment"
authors = [{name = "Your Name", email = "you@example.com"}]
license = {text = "MIT"}
readme = "README.md"
requires-python = ">=3.9"
dependencies = ["transformers>=4.30", "torch>=2.0"]
[project.urls]
Homepage = "https://github.com/you/bangla-nlp-toolkit"
"""
# my_nlp_tool/analyzer.py
from transformers import pipeline
class BanglaSentiment:
"""Bengali sentiment analyzer using pretrained model."""
def __init__(self, model_name="csebuetnlp/banglabert"):
self.pipe = pipeline("sentiment-analysis", model=model_name)
def analyze(self, text: str) -> dict:
result = self.pipe(text)[0]
return {"label": result["label"], "score": round(result["score"], 4)}
def batch_analyze(self, texts: list) -> list:
return [self.analyze(t) for t in texts]
# Build & publish
# pip install build twine
# python -m build
# twine upload dist/*
# Now anyone can: pip install bangla-nlp-toolkit
from bangla_nlp_toolkit import BanglaSentiment
analyzer = BanglaSentiment()
print(analyzer.analyze("আজকের দিনটা দারুণ!"))
# {'label': 'POSITIVE', 'score': 0.987}এই example একটা complete pip-installable Bengali NLP package। `pyproject.toml` package metadata define করে। `BanglaSentiment` class টা একটা reusable API। `python -m build` দিয়ে wheel তৈরি, `twine upload` দিয়ে PyPI তে publish। এরপর world wide কেউ `pip install` করে use করতে পারে।
PyPI তে publish-able একটা Bengali NLP toolkit — tokenizer, stemmer, sentiment, NER সব একসাথে। GitHub এ MIT license, full README, examples folder, GitHub Actions CI, এবং documentation site (MkDocs)। Target: ১০০+ stars।