Sentence Similarity
sentence-transformers
Safetensors
Transformers
Dutch
xlm-roberta
feature-extraction
Generated from Trainer
text-embeddings-inference
Instructions to use clips/e5-base-trm-nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use clips/e5-base-trm-nl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("clips/e5-base-trm-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-base-trm-nl with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("clips/e5-base-trm-nl") model = AutoModel.from_pretrained("clips/e5-base-trm-nl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cee1494a3118d3630814ee53e8631e245681547c948cc084a81d26e737d9a526
- Size of remote file:
- 6.1 kB
- SHA256:
- b919fee60ec36944071470770afb68de35a28fbbe9f9c3b6821a90f899ddf081
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