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") - Notebooks
- Google Colab
- Kaggle
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Browse files
README.md
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Parameters of the fit()-Method:
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{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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Parameters of the fit()-Method:
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