Sentence Similarity
sentence-transformers
Safetensors
Transformers
Dutch
bert
feature-extraction
text-embeddings-inference
Instructions to use clips/e5-small-trm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use clips/e5-small-trm with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("clips/e5-small-trm") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use clips/e5-small-trm with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("clips/e5-small-trm") model = AutoModel.from_pretrained("clips/e5-small-trm") - Notebooks
- Google Colab
- Kaggle
File size: 299 Bytes
4850d0f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"model_type": "SentenceTransformer",
"__version__": {
"sentence_transformers": "5.1.0",
"transformers": "4.56.1",
"pytorch": "2.7.1+cu126"
},
"prompts": {
"query": "query: ",
"document": "passage: "
},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |