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
Upload config.json with huggingface_hub
Browse files- config.json +2 -2
config.json
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{
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"_name_or_path": "/content/drive/MyDrive/
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"architectures": [
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"XLMRobertaModel"
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],
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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{
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"_name_or_path": "/content/drive/MyDrive/model11.bin/",
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"architectures": [
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"XLMRobertaModel"
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],
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.35.2",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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