Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use nthakur/RetroMAE_BEIR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nthakur/RetroMAE_BEIR with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nthakur/RetroMAE_BEIR") 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 nthakur/RetroMAE_BEIR with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nthakur/RetroMAE_BEIR") model = AutoModel.from_pretrained("nthakur/RetroMAE_BEIR") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91333cb64fd7da19650dc7f98faff9a97cbe749287fe4646ca056b97424fc0ed
- Size of remote file:
- 438 MB
- SHA256:
- f6a720644cfe4f41c317b73af9afb3aa837c7b3dbda0f54182a849fc3b36da1b
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