Arabic SBERT 100K
BERT-based Arabic sentence embedding model supporting semantic text similarity tasks
Downloads 770
Release Time : 7/26/2024
Model Overview
This model is a fine-tuned sentence transformer based on aubmindlab/bert-base-arabertv02, capable of mapping Arabic sentences and paragraphs into a 768-dimensional dense vector space, suitable for semantic search, text classification, and similar tasks.
Model Features
Arabic Language Optimization
Specially optimized for Arabic text, better handling of unique Arabic linguistic features
Efficient Vector Representation
Converts text into 768-dimensional dense vectors, preserving semantic information while maintaining computational efficiency
Multi-task Support
Supports various downstream tasks including semantic similarity calculation, semantic search, and text classification
Model Capabilities
Semantic Text Similarity Calculation
Semantic Search
Paraphrase Mining
Text Classification
Text Clustering
Use Cases
Information Retrieval
Arabic Document Search
Semantically matches relevant Arabic documents based on queries
Improves search result relevance
Content Analysis
Arabic News Classification
Classifies Arabic news articles based on content similarity
🚀 Arabic-SBERT-100K
This is a fine-tuned sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space, useful for semantic textual similarity, search, and more.
🚀 Quick Start
First, install the Sentence Transformers library:
pip install -U sentence-transformers
Then, you can load this model and run inference:
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'ما هو نوع الدهون الموجودة في الأفوكادو',
'حوالي 15 في المائة من الدهون في الأفوكادو مشبعة ، مع كل كوب واحد من الأفوكادو المفروم يحتوي على 3.2 جرام من الدهون المشبعة ، وهو ما يمثل 16 في المائة من DV البالغ 20 جرامًا. تحتوي الأفوكادو في الغالب على دهون أحادية غير مشبعة ، مع 67 في المائة من إجمالي الدهون ، أو 14.7 جرامًا لكل كوب مفروم ، ويتكون من هذا النوع من الدهون.',
'يمكن أن يؤدي ارتفاع مستوى الدهون الثلاثية ، وهي نوع من الدهون (الدهون) في الدم ، إلى زيادة خطر الإصابة بأمراض القلب ، ويمكن أن يؤدي توفير مستوى مرتفع من الدهون الثلاثية ، وهي نوع من الدهون (الدهون) في الدم ، إلى زيادة خطر الإصابة بأمراض القلب. مرض.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
✨ Features
- Semantic Understanding: Maps sentences and paragraphs to a 768-dimensional dense vector space for semantic analysis.
- Multiple Applications: Can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
📚 Documentation
Model Details
Model Description
Property | Details |
---|---|
Model Type | Sentence Transformer |
Base model | aubmindlab/bert-base-arabertv02 |
Maximum Sequence Length | 512 tokens |
Output Dimensionality | 768 tokens |
Similarity Function | Cosine Similarity |
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
Training Details
Training Dataset
- Unnamed Dataset
- Size: 75,000 training samples
- Columns:
anchor
,positive
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 4 tokens
- mean: 12.88 tokens
- max: 58 tokens
- min: 4 tokens
- mean: 13.74 tokens
- max: 126 tokens
- min: 4 tokens
- mean: 13.38 tokens
- max: 146 tokens
- Samples:
anchor positive negative هل تشاجر (سي إس لويس) و (جي آر آر تولكين) ؟ إن كان الأمر كذلك، فما هو السبب؟
هل صحيح أن (سي إس لويس) و (تولكين) تشاجرا؟
ما هي أفضل الكتب للدراسة في الجامعة؟
ما هي اعراض فقر الدم؟
ما هي اعراض الانيميا؟
كيف احضر كيكة العسل؟
من ستصوت له، دونالد ترامب أم هيلاري كلينتون؟
هل تؤيدون دونالد ترامب أم هيلاري كلينتون؟ لماذا؟
كيف أتغلب على إدمان المواد الإباحية؟
- Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 }
Evaluation Dataset
- Unnamed Dataset
- Size: 25,000 evaluation samples
- Columns:
anchor
,positive
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 4 tokens
- mean: 12.6 tokens
- max: 70 tokens
- min: 4 tokens
- mean: 14.82 tokens
- max: 239 tokens
- min: 4 tokens
- mean: 13.78 tokens
- max: 128 tokens
- Samples:
anchor positive negative نعم , نعم , أو رأيت " تشيما بارا ديسو "
نعم، أو "تشيما بارا ديسو" كانت تلك التي شاهدتها
أنا لم أرى "تشيما بارا ديسو".
رجل وامرأة يجلسان على الشاطئ بينما تغرب الشمس
هناك رجل وامرأة يجلسان على الشاطئ
إنهم يشاهدون شروق الشمس
كيف أسيطر على غضبي؟
ما هي أفضل طريقة للسيطرة على الغضب؟
كيف أعرف إن كانت زوجتي تخونني؟
- Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 16per_device_eval_batch_size
: 16learning_rate
: 2e-05num_train_epochs
: 5warmup_ratio
: 0.1fp16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: False
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