Instructions to use KennethEnevoldsen/dfm-sentence-encoder-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KennethEnevoldsen/dfm-sentence-encoder-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KennethEnevoldsen/dfm-sentence-encoder-small") 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] - Transformers
How to use KennethEnevoldsen/dfm-sentence-encoder-small with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KennethEnevoldsen/dfm-sentence-encoder-small") model = AutoModel.from_pretrained("KennethEnevoldsen/dfm-sentence-encoder-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4324ef55d14a2240cde156f96de32e34b89a98e5b49f7f3f361a27f144348e7
- Size of remote file:
- 87.6 MB
- SHA256:
- 984abbe1c4dde021ca735fb50b192068f3339830d4e33176c67a18532a3021fa
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