Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:1259
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use s1nju/darija-embedding-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use s1nju/darija-embedding-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("s1nju/darija-embedding-model") sentences = [ "الخبز تاع الشوفان صحي بزاف ومفيد للقلب.", "التعليم ماشي غير هدرة، هو رسالة وبناء اجيال.", "خصني دوش سخون يريحلي راسي.", "كي تاكل خبز شوفان مع العسل تحس بالطاقة." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from s1nju/darija-embedding-model: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/s1nju/darija-embedding-model/resolve/main/modules.json
- Command line
-
hf download hf://s1nju/darija-embedding-model/modules.json
-
curl -L -o modules.json https://huggingface.co/s1nju/darija-embedding-model/resolve/main/modules.json
229 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |