Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use BlueAvenir/TestDummy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BlueAvenir/TestDummy with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BlueAvenir/TestDummy") 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/TestDummy with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BlueAvenir/TestDummy") model = AutoModel.from_pretrained("BlueAvenir/TestDummy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_head.pkl from BlueAvenir/TestDummy: direct link, hf CLI and curl.
- Browser
- Download file 6.99 kB
-
https://huggingface.co/BlueAvenir/TestDummy/resolve/main/model_head.pkl
- Command line
-
hf download hf://BlueAvenir/TestDummy/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/BlueAvenir/TestDummy/resolve/main/model_head.pkl
6.99 kB
- Xet hash:
- e7b88f5af3949859d8e89352650a95f8a3ce1e170c92d85d9eda6608575a3554
- Size of remote file:
- 6.99 kB
- SHA256:
- 1fa12a8b131511c5c7b3fed21e8b0eab842d9e1f58b3e2c9b98b34bf60f8f062
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