Sentence Similarity
sentence-transformers
Safetensors
Transformers
Dutch
roberta
feature-extraction
Generated from Trainer
text-embeddings-inference
Instructions to use clips/robbert-2023-base-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use clips/robbert-2023-base-ft with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("clips/robbert-2023-base-ft") 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/robbert-2023-base-ft with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("clips/robbert-2023-base-ft") model = AutoModel.from_pretrained("clips/robbert-2023-base-ft") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0861298c405950f968e6ca8d7575ddd1c2addf13089b69cc4b23a0c2c9915e8
- Size of remote file:
- 6.1 kB
- SHA256:
- 53fbe67d95a1558d0d13b731a3579206d3d4b879a838328acfcdd507051d214d
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