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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
link to RobBERT fixed
Browse files
README.md
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# RobBERT-2023-base-ft
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RobBERT-2023-base-ft is a fine-tuned version of [DTAI-KULeuven/robbert-2023-dutch-base](
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## Usage
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# RobBERT-2023-base-ft
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RobBERT-2023-base-ft is a fine-tuned version of [DTAI-KULeuven/robbert-2023-dutch-base](https://huggingface.co/DTAI-KULeuven/robbert-2023-dutch-base). It demonstrates strong performance on MTEB-NL. If you’re looking for a state-of-the-art model of comparable size, you may also want to consider [clips/e5-base-trm-nl](https://huggingface.co/clips/e5-base-trm-nl).
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## Usage
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