Add MIT License and update README to include license information
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MIT License
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Copyright (c) 2025 Cyril Decostanzi
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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72
README.md
72
README.md
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@ -67,6 +67,7 @@ poetry install
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This will automatically install all dependencies declared in `pyproject.toml`, including **transformers** and **torch**.
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To run the CLI inside the Poetry environment:
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```bash
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poetry run python src/main.py
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```
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@ -118,12 +119,14 @@ Available commands:
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### Example Sessions
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#### 🔹 Sentiment Analysis
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```text
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💬 Enter text: I absolutely love this project!
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→ Sentiment: POSITIVE (score: 0.998)
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```
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#### 🔹 Fill‑Mask
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```text
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💬 Enter text: The capital of France is [MASK].
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→ Predictions:
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@ -133,12 +136,14 @@ Available commands:
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```
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#### 🔹 Text Generation
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```text
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💬 Prompt: Once upon a time
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→ Output: Once upon a time there was a young AI learning to code...
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```
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#### 🔹 NER (Named Entity Recognition)
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```text
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💬 Enter text: Elon Musk founded SpaceX in California.
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→ Entities:
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@ -148,6 +153,7 @@ Available commands:
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```
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#### 🔹 Moderation
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```text
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💬 Enter text: I hate everything!
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→ Result: FLAGGED (toxic content detected)
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@ -186,13 +192,13 @@ The internal structure follows a clean **Command ↔ Pipeline ↔ Display** patt
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### Key Concepts
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| Layer | Description |
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|-------|--------------|
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| **CLI** | Manages user input/output, help menus, and navigation between commands. |
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| **Command** | Encapsulates a single user-facing operation (e.g., run sentiment). |
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| **Pipeline** | Wraps Hugging Face’s `transformers.pipeline()` to perform inference. |
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| **Display** | Handles clean console rendering (colored output, tables, JSON formatting). |
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| **Config** | Centralizes model names, limits, and global constants. |
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| Layer | Description |
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| ------------ | -------------------------------------------------------------------------- |
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| **CLI** | Manages user input/output, help menus, and navigation between commands. |
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| **Command** | Encapsulates a single user-facing operation (e.g., run sentiment). |
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| **Pipeline** | Wraps Hugging Face’s `transformers.pipeline()` to perform inference. |
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| **Display** | Handles clean console rendering (colored output, tables, JSON formatting). |
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| **Config** | Centralizes model names, limits, and global constants. |
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---
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@ -261,54 +267,36 @@ tests/
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## 🧰 Troubleshooting
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| Issue | Cause / Solution |
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|-------|------------------|
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| **`transformers` not found** | Check virtual environment activation. |
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| **Torch fails to install** | Install CPU-only version from PyTorch index. |
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| **Models download slowly** | Hugging Face caches them after first run. |
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| **Unicode / accents broken** | Ensure terminal encoding is UTF‑8. |
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| Issue | Cause / Solution |
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| ---------------------------- | -------------------------------------------- |
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| **`transformers` not found** | Check virtual environment activation. |
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| **Torch fails to install** | Install CPU-only version from PyTorch index. |
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| **Models download slowly** | Hugging Face caches them after first run. |
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| **Unicode / accents broken** | Ensure terminal encoding is UTF‑8. |
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---
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## 🧭 Development Guidelines
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- Keep **Command** classes lightweight — no ML logic inside them.
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- Reuse the **Pipeline Template** for new experiments.
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- Format outputs consistently via the `DisplayFormatter`.
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- Document all new models or commands in `README.md` and `settings.py`.
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- Keep **Command** classes lightweight — no ML logic inside them.
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- Reuse the **Pipeline Template** for new experiments.
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- Format outputs consistently via the `DisplayFormatter`.
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- Document all new models or commands in `README.md` and `settings.py`.
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---
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## 🧱 Roadmap
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- [ ] Add non-interactive CLI flags (`--text`, `--task`)
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- [ ] Add multilingual model options
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- [ ] Add automatic test coverage
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- [ ] Add logging and profiling utilities
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- [ ] Add export to JSON/CSV results
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- [ ] Add non-interactive CLI flags (`--text`, `--task`)
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- [ ] Add multilingual model options
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- [ ] Add automatic test coverage
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- [ ] Add logging and profiling utilities
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- [ ] Add export to JSON/CSV results
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---
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## 🪪 License
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## 📜 License
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You can include a standard open-source license such as **MIT** or **Apache 2.0** depending on your use case.
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This project is licensed under the [MIT License](./LICENSE) — feel free to use it, modify it, and share it!
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---
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## 🤝 Contributing
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This repository is meant as an **educational sandbox** for experimenting with Transformers.
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Pull requests are welcome for new models, better CLI UX, or educational improvements.
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---
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### ✨ Key Takeaways
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- Modular and pedagogical design for training environments
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- Clean separation between **I/O**, **ML logic**, and **UX**
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- Easily extensible architecture for adding custom pipelines
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- Perfect sandbox for students, researchers, and developers to learn modern NLP tools
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---
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> 🧩 Built for experimentation. Learn, break, and rebuild.
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