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dataset.py — Cross-Script Translation Fix
==========================================
INPUT : quote_text (Roman/IAST transliteration of Sanskrit)
TARGET : quote_devanagari (Devanagari script)
This is the CORRECT task: the model learns to transliterate / translate
Roman Sanskrit → Devanagari, which is a meaningful, learnable mapping
(far better than devanagari→devanagari reconstruction which teaches nothing).
KEY CHANGES from original:
1. _input_field = 'quote_text' (was 'quote_devanagari')
2. _target_field = 'quote_devanagari' (unchanged)
3. Separate source/target tokenizers — Roman and Devanagari have
completely different character sets; a shared BPE vocab forces the
model to learn both scripts in one embedding table, which wastes
capacity and confuses the attention mechanism.
4. Negative example generation fixed — reversal now operates on
DEVANAGARI target only (not accidentally on Roman source).
5. curriculum_sort uses target length (Devanagari) for difficulty proxy.
"""
from datasets import load_dataset
from torch.utils.data import Dataset
import torch
import torch.nn.functional as F
import random
class OptimizedSanskritDataset(Dataset):
def __init__(self, split='train', tokenizer=None, max_len=80, cfg=None,
src_tokenizer=None, tgt_tokenizer=None):
"""
Args:
tokenizer : shared tokenizer (legacy — used if src/tgt not provided)
src_tokenizer : tokenizer for quote_text (Roman script)
tgt_tokenizer : tokenizer for quote_devanagari (Devanagari script)
If None, falls back to shared `tokenizer`.
"""
from config import CONFIG
self.cfg = cfg or CONFIG
self.max_len = max_len
self.pad_id = 1
self.mask_id = self.cfg['diffusion']['mask_token_id']
self.include_negatives = self.cfg['data']['include_negative_examples']
# ── Tokenizer setup ───────────────────────────────────────────
# Support both legacy (shared) and new (separate src/tgt) tokenizers
self.src_tokenizer = src_tokenizer or tokenizer
self.tgt_tokenizer = tgt_tokenizer or tokenizer
if self.src_tokenizer is None:
raise ValueError("Provide at least one tokenizer.")
print(f"📥 Loading '{split}' split …")
raw = load_dataset("paws/sanskrit-verses-gretil", split=split)
cols = raw.column_names
# ── Field selection ───────────────────────────────────────────
if 'quote_text' in cols and 'quote_devanagari' in cols:
# CORRECT setup: Roman input → Devanagari output
self._input_field = 'quote_text'
self._target_field = 'quote_devanagari'
print(" Format: quote_text (Roman) → quote_devanagari (Devanagari) ✓")
elif 'sentence1' in cols and 'sentence2' in cols:
# PAWS paraphrase pairs fallback
self._input_field = 'sentence1'
self._target_field = 'sentence2'
print(" Format: PAWS sentence pairs ✓")
else:
# Last resort: same field both sides
self._input_field = 'quote_devanagari'
self._target_field = 'quote_devanagari'
print(" ⚠️ Format: Devanagari→Devanagari (suboptimal — no quote_text found)")
# ── Filter empty rows ─────────────────────────────────────────
# Some rows have empty quote_text — skip them
raw = raw.filter(
lambda ex: (
bool(ex[self._input_field].strip()) and
bool(ex[self._target_field].strip())
)
)
print(f" After empty-filter: {len(raw)} samples")
self.dataset = raw
if split == 'train':
self.dataset = self._curriculum_sort()
print(f"✅ {len(self.dataset)} samples loaded.")
# ── Encoding ──────────────────────────────────────────────────────
def _encode_src(self, text):
"""Encode source (Roman) text."""
ids = self.src_tokenizer.encode(text)[:self.max_len]
t = torch.tensor(ids, dtype=torch.long)
t = F.pad(t, (0, max(0, self.max_len - len(t))), value=self.pad_id)
return t
def _encode_tgt(self, text):
"""Encode target (Devanagari) text."""
ids = self.tgt_tokenizer.encode(text)[:self.max_len]
t = torch.tensor(ids, dtype=torch.long)
t = F.pad(t, (0, max(0, self.max_len - len(t))), value=self.pad_id)
return t
# ── Curriculum ────────────────────────────────────────────────────
def _curriculum_sort(self):
"""Short, common Devanagari targets first → long, rare targets last."""
scores = []
for s in self.dataset:
text = s[self._target_field]
length = len(text.split())
rarity_score = len(set(text)) / max(1, len(text))
scores.append(length * (1 - rarity_score))
order = sorted(range(len(self.dataset)), key=lambda i: scores[i])
return self.dataset.select(order)
# ── Item ──────────────────────────────────────────────────────────
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
sample = self.dataset[idx]
src_text = sample[self._input_field].strip()
tgt_text = sample[self._target_field].strip()
input_ids = self._encode_src(src_text) # Roman encoded with src_tokenizer
target_ids = self._encode_tgt(tgt_text) # Devanagari encoded with tgt_tokenizer
out = {
'input_ids': input_ids,
'target_ids': target_ids,
'input_text': src_text,
'target_text': tgt_text,
}
if self.include_negatives:
neg_ids = target_ids.clone()
# Reverse a random chunk of the DEVANAGARI target
non_pad = (neg_ids != self.pad_id).sum().item()
if non_pad > 4:
i1, i2 = sorted(random.sample(range(non_pad), 2))
neg_ids[i1:i2] = torch.flip(neg_ids[i1:i2], dims=[0])
out['negative_target_ids'] = neg_ids
return out |