Sentence Similarity
sentence-transformers
Safetensors
English
roberta
feature-extraction
emotion
contrastive-learning
multi-label
text-embeddings-inference
Instructions to use foudil/lens-emotion-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use foudil/lens-emotion-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("foudil/lens-emotion-encoder") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 10c071547683c72e8edd4052ead579b6e59a4b7f5ae366f515180d735f8a06fa
- Size of remote file:
- 1.84 MB
- SHA256:
- 8d6bb59908a3f4606cc248d223979391c19a40ee13cd042b8bec68271113861b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.