Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use KennethEnevoldsen/dfm-sentence-encoder-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KennethEnevoldsen/dfm-sentence-encoder-medium with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KennethEnevoldsen/dfm-sentence-encoder-medium") 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-medium with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KennethEnevoldsen/dfm-sentence-encoder-medium") model = AutoModel.from_pretrained("KennethEnevoldsen/dfm-sentence-encoder-medium") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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oid sha256:5cfb62a076b64fed8b5d4dd50f2f91576aa5d7f24f9599a67b361fd4dc3a4cd3
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size 497810384
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