Feature Extraction
Transformers
Safetensors
sentence-transformers
embeddings
lora
sociology
retrieval
Instructions to use CodeSoulco/THETA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodeSoulco/THETA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="CodeSoulco/THETA")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CodeSoulco/THETA", device_map="auto") - sentence-transformers
How to use CodeSoulco/THETA with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CodeSoulco/THETA") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Commit History
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Update model card: add research links, base models, and official citation
Verified signatureHi! I'm Niels from the community science team at Hugging Face. I've improved your model card by: - Adding a link to the research paper: [THETA: A Textual Hybrid Embedding-based Topic Analysis Framework and AI Scientist Agent for Scalable Computational Social Science](https://huggingface.co/papers/2603.05972). - Adding a link to the official [GitHub repository](https://github.com/CodeSoul-co/THETA). - Adding the `base_model` metadata to link this repository to the foundation Qwen3-Embedding models. - Updating the citation section with the official BibTeX from your research. This will help users discover the context of your work and cite it correctly!6702ad3
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