Sentence Similarity
sentence-transformers
Safetensors
neobert
feature-extraction
dense
arabic
custom_code
Instructions to use U4RASD/NeoAraBERT-STS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use U4RASD/NeoAraBERT-STS with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("U4RASD/NeoAraBERT-STS", trust_remote_code=True) 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] - Notebooks
- Google Colab
- Kaggle
File size: 1,314 Bytes
1328c9a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | {
"architectures": [
"NeoBERT"
],
"auto_map": {
"AutoConfig": "model.NeoBERTConfig",
"AutoModel": "model.NeoBERT",
"AutoModelForMaskedLM": "model.NeoBERTLMHead",
"AutoModelForSequenceClassification": "model.NeoBERTForSequenceClassification"
},
"classifier_init_range": 0.02,
"decoder_init_range": 0.02,
"dim_head": 64,
"dtype": "float32",
"embedding_init_range": 0.02,
"hidden_size": 768,
"intermediate_size": 3072,
"kwargs": {
"architectures": [
"NeoBERTLMHead"
],
"attn_implementation": null,
"auto_map": {
"AutoConfig": "model.NeoBERTConfig",
"AutoModel": "model.NeoBERT",
"AutoModelForMaskedLM": "model.NeoBERTLMHead",
"AutoModelForSequenceClassification": "model.NeoBERTForSequenceClassification"
},
"classifier_init_range": 0.02,
"dim_head": 64,
"kwargs": {
"classifier_init_range": 0.02,
"trust_remote_code": true
},
"model_type": "neobert",
"torch_dtype": "float32",
"transformers_version": "4.48.2",
"trust_remote_code": true
},
"max_length": 1024,
"model_type": "neobert",
"norm_eps": 1e-05,
"num_attention_heads": 12,
"num_hidden_layers": 28,
"pad_token_id": 0,
"transformers_version": "4.57.0",
"trust_remote_code": true,
"vocab_size": 65000
}
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