Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use ng3owb/sentiment-embedding-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ng3owb/sentiment-embedding-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ng3owb/sentiment-embedding-model") 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
Download regressor.pt from ng3owb/sentiment-embedding-model: direct link, hf CLI and curl.
- Browser
- Download file 2.1 MB
-
https://huggingface.co/ng3owb/sentiment-embedding-model/resolve/main/regressor.pt
- Command line
-
hf download hf://ng3owb/sentiment-embedding-model/regressor.pt
-
curl -L -o regressor.pt https://huggingface.co/ng3owb/sentiment-embedding-model/resolve/main/regressor.pt
2.1 MB
- Xet hash:
- 0e7a34ad125bbc28367f9e9ee671f76d025371e8e7e5af125d78413f93be582c
- Size of remote file:
- 2.1 MB
- SHA256:
- dc2e70fc238dd34030c50242ccf8dba3d129495cd9d7f9670836bfee58c0e433
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