Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use gemasphi/laprador_mmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gemasphi/laprador_mmarco with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gemasphi/laprador_mmarco") 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 gemasphi/laprador_mmarco with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gemasphi/laprador_mmarco") model = AutoModel.from_pretrained("gemasphi/laprador_mmarco", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gemasphi/laprador_mmarco: direct link, hf CLI and curl.
- Browser
- Download file 266 MB
-
https://huggingface.co/gemasphi/laprador_mmarco/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gemasphi/laprador_mmarco/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gemasphi/laprador_mmarco/resolve/main/pytorch_model.bin
266 MB
- Xet hash:
- a7c6799dce93249e563cc8303a73611fa89c23a5f674f00e7aaf05d100e9ee9f
- Size of remote file:
- 266 MB
- SHA256:
- b10e354dba4b8567e813dc039eea19fb584caeb03154dae9ec2fc9e6ac8fc044
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