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:
# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from clips/robbert-2023-base-ft: direct link, hf CLI and curl.
- Browser
- Download file 249 MB
-
https://huggingface.co/clips/robbert-2023-base-ft/resolve/main/model.safetensors
- Command line
-
hf download hf://clips/robbert-2023-base-ft/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/clips/robbert-2023-base-ft/resolve/main/model.safetensors
249 MB
- Xet hash:
- f79b74978de10fbfbd7b56ebf77e2b2ba1269d924bc3a1403698f970994df0b6
- Size of remote file:
- 249 MB
- SHA256:
- f3b277241c9ce75da92712d683b43c6af1d6ecacd9fc08a67884432f7525e4ef
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