Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use prodm93/lb_large_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use prodm93/lb_large_base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("prodm93/lb_large_base") 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 prodm93/lb_large_base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("prodm93/lb_large_base") model = AutoModel.from_pretrained("prodm93/lb_large_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from prodm93/lb_large_base: direct link, hf CLI and curl.
- Browser
- Download file 1.35 GB
-
https://huggingface.co/prodm93/lb_large_base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://prodm93/lb_large_base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/prodm93/lb_large_base/resolve/main/pytorch_model.bin
1.35 GB
- Xet hash:
- 05b054de0ce4200019432c661c0fc217468c30d586f4e8fd0377f8a06920d65c
- Size of remote file:
- 1.35 GB
- SHA256:
- 2d536d6702dc9a621566dc072d936eb3658913c54d82e31bf67d1206ca304e7b
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