Sentence Similarity
sentence-transformers
Safetensors
Transformers
Dutch
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use clips/e5-large-trm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use clips/e5-large-trm with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("clips/e5-large-trm") 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-large-trm with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("clips/e5-large-trm") model = AutoModel.from_pretrained("clips/e5-large-trm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- acdcd7b143eff1d04672fa77bef1abb11af28eed48c605c7824f302c5c7d70a2
- Size of remote file:
- 1.42 GB
- SHA256:
- 11cd7826f3099f291bea9746f7f96025e7ac59d153752f12f26af839b3d3fff3
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