Sentence Similarity
sentence-transformers
Safetensors
Transformers
Dutch
bert
feature-extraction
Generated from Trainer
text-embeddings-inference
Instructions to use clips/e5-small-v2-t2t-nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use clips/e5-small-v2-t2t-nl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("clips/e5-small-v2-t2t-nl") 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-v2-t2t-nl with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("clips/e5-small-v2-t2t-nl") model = AutoModel.from_pretrained("clips/e5-small-v2-t2t-nl") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29842b9aaadd5822496d1b93295f97ef13344cb0298a10e744104530c3ee4452
- Size of remote file:
- 6.1 kB
- SHA256:
- d2bed1467f6dc5994f9a593f36eb2818d6985dd2957c3b267608e8c19dca1ef2
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