README.md

---
license: mit
language:
  - zh
  - en
  - de
  - fr
task_categories:
  - feature-extraction
  - text-classification
tags:
  - embeddings
  - sociology
  - retrieval
  - sentence-transformers
  - numpy
  - qwen3
pretty_name: THETA Embeddings
---

# THETA-embeddings

Pre-computed embeddings generated by [THETA](https://huggingface.co/CodeSoulco/THETA), a domain-specific embedding model fine-tuned on Qwen3-Embedding for sociology and social science texts.

## Description

This dataset contains dense vector embeddings produced under three settings:

- **zero_shot:** Embeddings from the base Qwen3-Embedding model without fine-tuning
- **supervised:** Embeddings from the LoRA-adapted model trained with label-guided contrastive learning
- **unsupervised:** Embeddings from the LoRA-adapted model trained with SimCSE

## Repository Structure

```
CodeSoulco/THETA-embeddings/
├── 0.6B/
│   ├── zero_shot/
│   ├── supervised/
│   └── unsupervised/
└── 4B/
    ├── zero_shot/
    ├── supervised/
    └── unsupervised/
```

## Embedding Details

| Model | Dimension | Format |
|---|---|---|
| Qwen3-Embedding-0.6B | 896 | `.npy` |
| Qwen3-Embedding-4B | 2560 | `.npy` |

**Source Datasets:** germanCoal, FCPB, socialTwitter, hatespeech, mental_health

## How to Use

```python
import numpy as np

# Load pre-computed embeddings
embeddings = np.load("0.6B/zero_shot/germanCoal_zero_shot_embeddings.npy")
print(embeddings.shape)  # (num_samples, 896)
```

Or download via `huggingface_hub`:

```python
from huggingface_hub import hf_hub_download
import numpy as np

path = hf_hub_download(
    repo_id="CodeSoulco/THETA-embeddings",
    filename="0.6B/supervised/socialTwitter_supervised_embeddings.npy",
    repo_type="dataset"
)
embeddings = np.load(path)
```

## Related

- **Model (LoRA weights):** [CodeSoulco/THETA](https://huggingface.co/CodeSoulco/THETA)

## License

This dataset is released under the **MIT License**.

## Citation

```bibtex
@misc{theta2026,
  title={THETA: Textual Hybrid Embedding--based Topic Analysis},
  author={CodeSoul},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/CodeSoulco/THETA}
}
```