Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
linktransformer
tabular-classification
text-embeddings-inference
Instructions to use dell-research-harvard/lt-wikidata-comp-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dell-research-harvard/lt-wikidata-comp-multi with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dell-research-harvard/lt-wikidata-comp-multi") sentences = [ "Das ist eine glückliche Person", "Das ist ein glücklicher Hund", "Das ist eine sehr glückliche Person", "Heute ist ein sonniger Tag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Updated model with better training and evaluation. Test and val data included as pickle files. Older Legacy files were removed to avoid confusion.
129b41d - Xet hash:
- 2c3c2e52cef84fcd1cf1b8556e34d627e01a20eab17e5b8d692dad72f498133a
- Size of remote file:
- 22.6 MB
- SHA256:
- f789fa76be8971dd2c94a65a27f841b15ebdd2644e0b5e50eb704e81ff98e451
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