Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
colbert
late-interaction
retrieval
pylate
text-embeddings-inference
Instructions to use chungimungi/GLInt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use chungimungi/GLInt with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("chungimungi/GLInt") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Fix serialization so the model loads with public PyLate 1.6.0 (metadata only; weights unchanged)
fcbd9fa verified | [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Dense", | |
| "type": "pylate.models.Dense.Dense" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Dense", | |
| "type": "pylate.models.Dense.Dense" | |
| }, | |
| { | |
| "idx": 3, | |
| "name": "3", | |
| "path": "3_Dense", | |
| "type": "pylate.models.Dense.Dense" | |
| } | |
| ] |