# MT History — Reproducible Machine Translation from 1949

An interactive course tracing the full history of machine translation,
from Shannon (1948) and Weaver (1949) through LLM-based MT (2023+).
Every model is implemented from scratch in Python.
Every chapter runs in a free Colab session.

**Live site:** https://eduardosanchezg.github.io/mthistory

## Quick start

```bash
# Download the corpus (optional — chapters fall back to a built-in sample)
python data/download_multi30k.py

# Parts 0–2: no dependencies
python -c "import notebook"   # any Jupyter will do
jupyter lab notebooks/part0/00_shannon.ipynb

# Parts 3+: install per-part deps with uv
uv sync --group part3
uv run jupyter lab notebooks/part3/31_ibm_model1.ipynb
```

## Open in Colab

Every notebook has an "Open in Colab" badge and a `!pip install` header cell
with pinned versions, so you can run any chapter in one click.

## Reproducibility

| Parts | Dependencies | Runs on |
|-------|-------------|---------|
| 0–2   | stdlib only | Any Python 3.8+ |
| 3     | numpy, scipy | Any laptop |
| 4     | torch (CPU) | Any laptop |
| 5     | torch | Codespaces / Colab GPU |
| 6     | transformers, sentencepiece | Colab / Codespaces |

See `.devcontainer/` for a one-click GitHub Codespaces setup.

## Structure

```
notebooks/
  part0/   Before MT Was MT (1948–1949)
  part1/   Direct Transfer / RBMT (1954–1970)
  part2/   Example-Based MT (1984–1995)
  part3/   Statistical MT (1990–2014)
  part4/   Neural MT (2013–2017)
  part5/   Transformer Era (2017–)
  part6/   Multilingual & Low-Resource MT (2019–)
  part7/   Evaluation
```

## CI

GitHub Actions runs every notebook on every push via `nbmake`.
Neural chapters use `TRAIN_FROM_SCRATCH = False` and load pre-trained
checkpoints from `checkpoints/` so CI passes on CPU.