Sentence Similarity
sentence-transformers
Safetensors
phi3
feature-extraction
dense
Generated from Trainer
dataset_size:5749
loss:CosineSimilarityLoss
custom_code
Eval Results (legacy)
Instructions to use AntoineGourru/FT_phi_cos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AntoineGourru/FT_phi_cos with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AntoineGourru/FT_phi_cos", trust_remote_code=True) sentences = [ "A man is playing the guitar.", "A man plays an acoustic guitar.", "Ryanair chief hails report findings", "2 British soldiers killed in Afghan insider attack" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from AntoineGourru/FT_phi_cos: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/AntoineGourru/FT_phi_cos/resolve/main/modules.json
- Command line
-
hf download hf://AntoineGourru/FT_phi_cos/modules.json
-
curl -L -o modules.json https://huggingface.co/AntoineGourru/FT_phi_cos/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" | |
| } | |
| ] |