Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use BlueAvenir/test_negative_positive_class_mode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BlueAvenir/test_negative_positive_class_mode with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BlueAvenir/test_negative_positive_class_mode") 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/test_negative_positive_class_mode with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BlueAvenir/test_negative_positive_class_mode") model = AutoModel.from_pretrained("BlueAvenir/test_negative_positive_class_mode", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from BlueAvenir/test_negative_positive_class_mode: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/BlueAvenir/test_negative_positive_class_mode/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://BlueAvenir/test_negative_positive_class_mode/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/BlueAvenir/test_negative_positive_class_mode/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 31c0c6821f9ba4407d33335c4549be45b7741d89a517129751768c687e0a2feb
- Size of remote file:
- 1.11 GB
- SHA256:
- 45173a685ed1e800826d7652e4ad257dcf3fd7b3f917e0b4df5b004fb2d64559
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