NAMAA-retriever-contrastive-final

SentenceTransformer based on yoriis/NAMAA-retriever-contrastive-2

This is a sentence-transformers model finetuned from yoriis/NAMAA-retriever-contrastive-2. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: yoriis/NAMAA-retriever-contrastive-2 <!-- at revision 313a50bc2e0efdcdb1b119fc40ca91df096e3720 -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

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: ModernBertModel 
  (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})
)

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("yoriis/NAMAA-retriever-contrastive-final")
# Run inference
sentences = [
    'ما الأحوال التي يسقط فيها استقبال القبلة؟',
    'أحسب الناس أن يتركوا أن يقولوا آمنا وهم لا يفتنون{2} ولقد فتنا الذين من قبلهم فليعلمن الله الذين صدقوا وليعلمن الكاذبين{3} العنكبوت',
    'وقفينا على آثارهم بعيسى ابن مريم مصدقا لما بين يديه من التوراة وآتيناه الإنجيل فيه هدى ونور ومصدقا لما بين يديه من التوراة وهدى وموعظة للمتقين {46}المائدة',
]
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]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 9,582 training samples
• Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  details	<ul><li>min: 5 tokens</li><li>mean: 10.97 tokens</li><li>max: 103 tokens</li></ul>	<ul><li>min: 12 tokens</li><li>mean: 159.81 tokens</li><li>max: 512 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.16</li><li>max: 1.0</li></ul>

• Samples:

  sentence_0	sentence_1	label
  <code>متى تكون التوبة غير مقبولة ؟</code>	<code>عن أنس رضي الله عنه أن رسول الله ﷺ قال: (إذا قُدِّم العَشَاءُ فابدؤوا به قبل أن تصلّوا المغربَ). متفق عليه</code>	<code>0.0</code>
  <code>ما حكم قول حي على خير العمل في الأذان ؟</code>	<code>جَابِرٍ رضي الله عنه، عَنِ النَّبِيِّ ﷺ قَالَ: «إِذَا كَانَ جُنْحُ اللَّيْلِ، أَوْ أَمْسَيْتُمْ، فَكُفُّوا صِبْيَانَكُمْ، فَإِنَّ الشَّيَاطِينَ تَنْتَشِرُ حِينَئِذٍ، فَإِذَا ذَهَبَ سَاعَةٌ مِنَ اللَّيْلِ فَحُلُّوهُمْ، فَأَغْلِقُوا الأَبْوَابَ وَاذْكُرُوا اسْمَ الله، فَإِنَّ الشَّيْطَانَ لاَ يَفْتَحُ بَابًا مُغْلَقًا، وَأَوْكُوا قِرَبَكُمْ وَاذْكُرُوا اسْمَ الله، وَخَمِّرُوا آنِيَتَكُمْ وَاذْكُرُوا اسْمَ الله، وَلَوْ أَنْ تَعْرُضُوا عَلَيْهَا شَيْئًا، وَأَطْفِئُوا مَصَابِيحَكُمْ». رواه البخاري (5623)، ومسلم (2012).</code>	<code>0.0</code>
  <code>من هو آخر الأنبياء ؟</code>	<code>حديث عَائِشَةَ رضي الله عنها، قَالَتْ: أَقْبَلَتْ فَاطِمَةُ تَمْشِي كَأَنَّ مِشْيَتَهَا مَشْيُ النَّبِيِّ ﷺ، فَقَالَ النَّبِيُّ ﷺ: «أَمَا تَرْضَيْنَ أَنْ تَكُونِي سَيِّدَةَ نِسَاءِ أَهْلِ الجَنَّةِ». رواه البخاري (3624)، ومسلم(2450)</code>	<code>0.0</code>

• Loss: <code>ContrastiveLoss</code> with these parameters:

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• fp16: True
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• 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: True
• 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
• hub_revision: None
• 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
• 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
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin

</details>

Training Logs

Framework Versions

• Python: 3.11.13
• Sentence Transformers: 4.1.0
• Transformers: 4.54.0
• PyTorch: 2.6.0+cu124
• Accelerate: 1.9.0
• Datasets: 4.0.0
• Tokenizers: 0.21.2

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",
}

ContrastiveLoss

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}

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