Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use kornwtp/simcse-model-XLMR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kornwtp/simcse-model-XLMR with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kornwtp/simcse-model-XLMR") 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 kornwtp/simcse-model-XLMR with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kornwtp/simcse-model-XLMR") model = AutoModel.from_pretrained("kornwtp/simcse-model-XLMR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kornwtp/simcse-model-XLMR: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/kornwtp/simcse-model-XLMR/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kornwtp/simcse-model-XLMR/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kornwtp/simcse-model-XLMR/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 50851dd0f6d4e0f5cca48a9c467c08f12293ed1c84c8fa62eb9dcc33939f4147
- Size of remote file:
- 1.11 GB
- SHA256:
- 63b1b1da812030e4f1bb2c3026d4aa2eb683fd7c62cddc6fa87b0387554bb1cb
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