Add from-scratch Transformer (test BLEU 22.96)

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+sample_translations.png filter=lfs diff=lfs merge=lfs -text
+sample_translations_best.png filter=lfs diff=lfs merge=lfs -text
+training_report.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,88 @@
+---
+license: mit
+language:
+  - si
+  - pi
+tags:
+  - translation
+  - transformer
+  - from-scratch
+  - low-resource
+  - pali
+  - sinhala
+pipeline_tag: translation
+---
+
+# Pāli → Sinhala Transformer (from scratch)
+
+An encoder–decoder Transformer implemented **from scratch** in PyTorch — no
+`nn.Transformer`, no pretrained weights — for Pāli → Sinhala translation.
+Both languages are written in Sinhala script in this corpus, so a single shared
+subword vocabulary is used and the embeddings are tied across the encoder,
+decoder and output projection.
+
+## Results
+
+| Split | BLEU | chrF |
+|---|---|---|
+| Test | **22.96** | **46.44** |
+| Validation | 22.86 | — |
+
+Metrics are sacreBLEU (13a tokenizer) and chrF, single reference.
+
+## Architecture
+
+| | |
+|---|---|
+| Layers | 5 encoder + 5 decoder (Pre-LN) |
+| d_model / d_ff / heads | 512 / 2048 / 8 |
+| Activation | GELU |
+| Parameters | ~38.8M |
+| Tokenizer | Shared SentencePiece **unigram**, 4000 vocab |
+| Embeddings | Tied (encoder ⊗ decoder ⊗ output) |
+
+## Training
+
+Trained for up to 300 epochs on ~16.5k parallel pairs with:
+
+- label smoothing 0.2, residual dropout 0.3
+- source word-dropout 0.1
+- **subword regularisation** (unigram sampling, α=0.1) — a fresh
+  segmentation every epoch, acting as data augmentation
+- token-bucketed batching (~3000 tokens × 2 accumulation)
+- AdamW, linear warmup (1000 steps) + inverse-sqrt decay, peak LR 0.0007
+- early stopping on **validation BLEU**, and **top-5 checkpoint averaging**
+
+Decoding uses beam search (beam **8**) with a GNMT length penalty
+α=**0.6**, both selected on the validation set.
+
+## Data preprocessing
+
+NFC Unicode normalisation → zero-width character stripping → whitespace
+squeezing → exact duplicate-pair removal (which also eliminates train/test
+leakage) → length window 1–80 words → length-ratio cap 4.0.
+Split 80/10/10.
+
+## Files
+
+- `model_weights.pt` — state dict (averaged checkpoint)
+- `spm.model` / `spm.vocab` — SentencePiece tokenizer
+- `metadata.json` — architecture + decoding config
+- `test_results.csv` — every test prediction with its sentence BLEU
+- figures: training report, attention map, tokenization analysis, sample translations
+
+## Usage
+
+The model classes are not packaged as a `transformers` architecture — see the
+accompanying Space for a self-contained `app.py` that rebuilds the model and
+loads this state dict:
+<https://huggingface.co/spaces/NethranjaliSE/Pali_Sinhala_Translator_From_Scratch>
+
+## Limitations
+
+Trained on a small, domain-specific corpus of canonical Buddhist text. Output is
+frequently fluent but **not always faithful** — the model can produce a
+well-formed Sinhala sentence that does not correspond to the Pāli source. It is
+a research prototype and should not be relied on for doctrinal or scholarly
+interpretation. Many Pāli lines have several valid Sinhala renderings, so
+single-reference BLEU understates quality somewhat.

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metadata.json

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+{
+  "model": "Transformer (from scratch, Pre-LN, tied embeddings)",
+  "task": "Pali → Sinhala MT",
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+  "spm_model_type": "unigram",
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+  "test_bleu": 22.9565,
+  "test_chrf": 46.436,
+  "checkpoint": "averaged",
+  "val_bleu": 22.855
+}

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