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:
# pip install -U transformers accelerate # 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
Commit History
-
Add new SentenceTransformer model (#1)
Verified signature- Add new SentenceTransformer model (08795862415fc07af1f3d6524b4e8c7c080c88fa) Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>ec2c577
-
README.md updated
Nikolay Banard9d6369 -
Update README.md
Verified signatureda7428f
-
commit files to HF hub
e8187c0 -
Upload tokenizer
Verified signature7b53e5f -
Upload model
Verified signature2ba2a1f -
initial commit
Verified signatureb9d220e