BhashaEmbed Hindi Songs — TSDAE Domain Adapted
Domain-adapted version of AkshitaS/bhasha-embed-v0
for Hindi film song lyrics.
Training
- Method: TSDAE full fine-tuning (no quantization, no LoRA)
- Fine-tuning: Full parameter fine-tuning (all weights updated)
- Corpus: 8,464 Hindi film songs, 28,713 verse-level chunks
- Scripts: Devanagari, Romanized Hindi (Hinglish), English — single unified model
- Script balancing: weighted sampling to 44/44/12 (Devanagari/Hinglish/English)
- Epochs: 5
- Batch size: 4
- Learning rate: 3e-05
- Max sequence length: 400 subword tokens
Intended Use
Sentence embeddings for Hindi film song lyrics across all three script forms. Designed for thematic classification and generational/temporal analysis of Hindi music.
Limitations
- Full parameter fine-tuning — no LoRA/quantization constraints on how far weights can move from the base model
- No supervised thematic fine-tuning applied (Phase A only)
- Not evaluated on tasks outside Hindi song lyrics
Usage
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("meet5568/bhash_finetune")
lyrics = [
"तेरे बिना जिंदगी से कोई शिकवा नहीं",
"tere bina zindagi se koi shikwa nahi",
"without you life has no complaint",
]
embeddings = model.encode(lyrics)
print(embeddings.shape) # (3, 768)