Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align") 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-large-exp2-no-lang-align with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align") model = AutoModel.from_pretrained("KennethEnevoldsen/dfm-sentence-encoder-large-exp2-no-lang-align") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ce6949eb1b329d5288d10f5dd1f48f119f8f439ae528bc165cd656b064b67ca
- Size of remote file:
- 1.42 GB
- SHA256:
- 26a8b697937c74ce6074216f9f2048b0b79f4c74c0256816e4714f4f93762a29
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