Sentence Similarity
sentence-transformers
Safetensors
new
feature-extraction
Generated from Trainer
dataset_size:170845
loss:BatchHardTripletLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use philmas/cese5020-contrastive-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use philmas/cese5020-contrastive-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("philmas/cese5020-contrastive-model", trust_remote_code=True) sentences = [ "Radionuclide therapy for targeted cancer treatment", "Reinforced fish plates with a zinc coating for tropical climates", "Tantalum alloy powders for hardened steel production.", "Kraft pulp used extensively in the production of kraft paper and bags." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from philmas/cese5020-contrastive-model: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/philmas/cese5020-contrastive-model/resolve/main/modules.json
- Command line
-
hf download hf://philmas/cese5020-contrastive-model/modules.json
-
curl -L -o modules.json https://huggingface.co/philmas/cese5020-contrastive-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" | |
| } | |
| ] |