Sentence Similarity
sentence-transformers
Safetensors
English
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:80
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use aamohame/hack_ai_embbedding_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aamohame/hack_ai_embbedding_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aamohame/hack_ai_embbedding_model") sentences = [ "A man with blond-hair, and a brown shirt drinking out of a public water fountain.", "A blond man wearing a brown shirt is reading a book on a bench in the park", "The people are standing still on the curb.", "An elderly man sits in a small shop." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 283 Bytes
d26a654 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"__version__": {
"pytorch": "2.10.0+cu128",
"sentence_transformers": "5.4.1",
"transformers": "5.0.0"
},
"default_prompt_name": null,
"model_type": "SentenceTransformer",
"prompts": {
"document": "",
"query": ""
},
"similarity_fn_name": "cosine"
} |