Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use intellya22/new-test-model-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intellya22/new-test-model-ar with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intellya22/new-test-model-ar") 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 intellya22/new-test-model-ar with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("intellya22/new-test-model-ar") model = AutoModel.from_pretrained("intellya22/new-test-model-ar", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9ab3bbc6f379e1d2d41936a753d4c808cfe8bd4c8be0eb7dde573498e6a7ce0
- Size of remote file:
- 438 MB
- SHA256:
- 15ba440e10cf33fb32160d4ddcca324c5286f144bec38724388dde951b20e460
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