AhmadAfles/NeoAraBERT-STS-Genre

SentenceTransformer based on U4RASD/NeoAraBERT-STS

This is a sentence-transformers model finetuned from U4RASD/NeoAraBERT-STS. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: U4RASD/NeoAraBERT-STS
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'NeoBERT'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

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 = [
    'لما تحلل أسباب فشل المشروع ده، هتلاقي إن الشركاء ركزوا على الدعاية والإعلان وصرفوا مبالغ ضخمة، ونسيوا يطوروا جودة المنتج الأساسي اللي بيبيعوه.',
    'أَظْهَرَتْ نَتَائِجُ فَرْزِ العَيِّنَاتِ الإِحْصَائِيَّةِ أَنَّ العَجْزَ فِي المِيزَانِ التِّجَارِيِّ يَعُودُ بِشَكْلٍ مُّبَاشِرٍ إِلَى ارْتِفَاعِ فَاتُورَةِ اسْتِيرَادِ مَوَادِّ الطَّاقَةِ.',
    'يُفَضَّلُ اسْتِخْدَامُ سَمَّاعَاتِ الرَّأْسِ المُحِيطِيَّةِ فِي أَلْعَابِ الشُّوتِر (المُواكَبَةِ لِلْمَعَارِكِ) لِتَحْدِيدِ مَكَانِ خُطُوَاتِ الأَعْدَاءِ بِدِقَّةٍ عَالِيَّةٍ.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7234, 0.3511],
#         [0.7234, 1.0000, 0.3113],
#         [0.3511, 0.3113, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

• Size: 465 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 100 samples:

  	sentence_0	sentence_1
  type	string	string
  modality	text	text
  details	min: 39 tokens mean: 63.68 tokens max: 93 tokens	min: 43 tokens mean: 63.62 tokens max: 88 tokens

• Samples:

  sentence_0	sentence_1
  يُنْصَحُ بِإِزَالَةِ المَكْيَاجِ بِاسْتِخْدَامِ تِقْنِيَّةِ 'التَّنْظِيفِ المُزْدَوَجِ'؛ أَيْ بِمُزِيلٍ زَيْتِيٍّ أَوَّلاً لِتَفْكِيكِ الدُّهُونِ، ثُمَّ بِالغَسُولِ المَائِيِّ.	تَمْتَازُ النَّظَّارَاتُ الشَّمْسِيَّةُ ذَاتُ الإِطَارِ الكَبِيرِ (عَيْن القِطَّةِ) بِقُدْرَتِهَا عَلَى إِبْرَازِ جَمَالِ المَلَامِحِ وَتَحْدِيدِ عِظَامِ الوَجْهِ بِأَنَاقَةٍ.
  بلاش تبالغوا في عدد الكوشيات أو المخدات الصغيرة على الكنبة، لو زادت عن حدها هتحسس اللي قاعد إن المكان مكركب وكمان هتضطروا تشيلوها كل شوية.	الأثاث الذكي أو متعدد الاستخدامات هو البطل الحقيقي في الشقق الصغيرة، يعني مثلاً سرير جواه سحارة للتخزين أو كنبة بتتحول سرير وقت اللزوم.
  التجارة الإلكترونية مش بس موقع، ده نظام متكامل بيبدأ من المخازن، مروراً بخدمة العملاء، وصولاً لشركة الشحن اللي بتوصل المنتج لبيت العميل.	تُسَاهِمُ إِعْلَانَاتُ رِيتَارْجِتِينْغ (Retargeting) فِي تَذْكِيرِ العُمَلَاءِ بِالمُنْتَجَاتِ الَّتِي أَبْدَوْا اهْتِمَاماً بِهَا، وَهِيَ اسْتْرَاتِيجِيَّةٌ فَعَّالَةٌ لِاسْتِعَادَةِ الزَّوَّارِ المُتَرَدِّدِينَ.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• num_train_epochs: 3
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin
• router_mapping: {}
• learning_rate_mapping: {}

Training Time

• Training: 1.8 minutes

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.6.0
• Transformers: 4.49.0
• PyTorch: 2.11.0+cu128
• Accelerate: 1.14.0
• Datasets: 4.0.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}