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
| { | |
| "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" | |
| } |