Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use BlueAvenir/TestCaseDummy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BlueAvenir/TestCaseDummy with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BlueAvenir/TestCaseDummy") 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 BlueAvenir/TestCaseDummy with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BlueAvenir/TestCaseDummy") model = AutoModel.from_pretrained("BlueAvenir/TestCaseDummy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from BlueAvenir/TestCaseDummy: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/BlueAvenir/TestCaseDummy/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://BlueAvenir/TestCaseDummy/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/BlueAvenir/TestCaseDummy/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- c6cf347c077e3d17b88cfc39e29acb86a91d75ae0555cd2a1c36c2311abd480e
- Size of remote file:
- 1.11 GB
- SHA256:
- 7f6b607ed6766b70141eff0a74d8a8fe70832cc4c266140bfd0244080654c02f
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