Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:295188
loss:WeightedMultiPositiveCachedLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Ehsanl/cncpt_e5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Ehsanl/cncpt_e5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Ehsanl/cncpt_e5") 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
Download 2_Normalize/config.json from Ehsanl/cncpt_e5: direct link, hf CLI and curl.
- Browser
- Download file 97 Bytes
-
https://huggingface.co/Ehsanl/cncpt_e5/resolve/main/2_Normalize/config.json
- Command line
-
hf download hf://Ehsanl/cncpt_e5/2_Normalize/config.json
-
curl -L -o config.json https://huggingface.co/Ehsanl/cncpt_e5/resolve/main/2_Normalize/config.json
97 Bytes
| { | |
| "module_input_name": "sentence_embedding", | |
| "module_output_name": "sentence_embedding" | |
| } |