Sentence Similarity
sentence-transformers
Safetensors
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use ramdane/jurimodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ramdane/jurimodel with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ramdane/jurimodel") 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 ramdane/jurimodel with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ramdane/jurimodel") model = AutoModel.from_pretrained("ramdane/jurimodel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c244270ad3e1ca8fb08fb9cf0d3110c371bcc97cd3976259a128e5b184ae98c5
- Size of remote file:
- 1.11 GB
- SHA256:
- 48dae4887148b56392fb418ad9a6a7dda21e7306d0e7a57d2ff6e205d25256bb
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