Sentence Similarity
sentence-transformers
Safetensors
Transformers
Dutch
roberta
feature-extraction
Generated from Trainer
text-embeddings-inference
Instructions to use clips/robbert-2023-large-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use clips/robbert-2023-large-ft with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("clips/robbert-2023-large-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-large-ft with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("clips/robbert-2023-large-ft") model = AutoModelForMultimodalLM.from_pretrained("clips/robbert-2023-large-ft") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ede752174e090f8d03c1feda44752ea795d16fbd09f732503e76372d63a7c749
- Size of remote file:
- 1.42 GB
- SHA256:
- fef0f10e564c2a15551d174df08398192900767a2c64dad65f4337a5067e3b50
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