Feature Extraction
sentence-transformers
Safetensors
Arabic
bert
sentence-similarity
dense
Generated from Trainer
dataset_size:2964
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use AzeerDev/SA-STS-Embeddings-0.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AzeerDev/SA-STS-Embeddings-0.2B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AzeerDev/SA-STS-Embeddings-0.2B") sentences = [ "كم تكلفة رحلة بحرية ليوم؟", "الباحثين يحلّلوا تأثير البيئة على اختلاف العادات بين المناطق.", "أبي محلات فيها بضاعة عالمية مشهورة.", "بكم أسعار الجولات البحرية اليومية؟" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "directionality": "bidi", | |
| "dtype": "float32", | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "pooler_fc_size": 768, | |
| "pooler_num_attention_heads": 12, | |
| "pooler_num_fc_layers": 3, | |
| "pooler_size_per_head": 128, | |
| "pooler_type": "first_token_transform", | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.57.2", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 100000 | |
| } | |