---
language:
  - hi
  - en
license: apache-2.0
library_name: sentence-transformers
tags:
  - sentence-transformers
  - feature-extraction
  - hindi
  - hinglish
  - code-mixed
  - song-lyrics
  - tsdae
base_model: AkshitaS/bhasha-embed-v0
---

# BhashaEmbed Hindi Songs — TSDAE Domain Adapted

Domain-adapted version of [`AkshitaS/bhasha-embed-v0`](https://huggingface.co/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

```python
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)
```