leafxyz/arabic-ecom-cross-encoder-v3

CrossEncoder based on cross-encoder/mmarco-mMiniLMv2-L12-H384-v1

This is a Cross Encoder model finetuned from cross-encoder/mmarco-mMiniLMv2-L12-H384-v1 on the arabic-ecom-data dataset using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

• —Model Type: Cross Encoder
• —Base model: cross-encoder/mmarco-mMiniLMv2-L12-H384-v1 <!-- at revision 1427fd652930e4ba29e8149678df786c240d8825 -->
• —Maximum Sequence Length: 128 tokens
• —Number of Output Labels: 1 label
• —Supported Modality: Text
• —Training Dataset:
• —arabic-ecom-data <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

• —Documentation: Sentence Transformers Documentation
• —Documentation: Cross Encoder Documentation
• —Repository: Sentence Transformers on GitHub
• —Hugging Face: Cross Encoders on Hugging Face

Full Model Architecture

CrossEncoder(
  (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'XLMRobertaForSequenceClassification'})
)

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 CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("leafxyz/arabic-ecom-cross-encoder-v3")
# Get scores for pairs of inputs
pairs = [
    ['مناديل مطبخ', 'صابون اواني جودي - 960 مل (الليمون الاخضر)'],
    ['جبنة هابي كاو', 'هابي كاو جبنة كريمى - 150 غ'],
    ['كريم تايغر للشعر', 'كريم ازالة شعر - Page Vine'],
    ['لانشون حلواني', 'لانشون حلواني دجاج - 250 غ'],
    ['صابون جودي 2.32', 'صابون اواني جودي برائحة الليمون الاخضر - 2.32 ل'],
]
scores = model.predict(pairs)
print(scores)
# [-5.0312  0.2981 -1.2588  0.6904  0.7002]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'مناديل مطبخ',
    [
        'صابون اواني جودي - 960 مل (الليمون الاخضر)',
        'هابي كاو جبنة كريمى - 150 غ',
        'كريم ازالة شعر - Page Vine',
        'لانشون حلواني دجاج - 250 غ',
        'صابون اواني جودي برائحة الليمون الاخضر - 2.32 ل',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

arabic-ecom-data

• —Dataset: arabic-ecom-data
• —Size: 246,013 training samples
• —Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
• —Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 3 tokens</li><li>mean: 7.77 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.06 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
• —Samples: | sentence1 | sentence2 | label | |:-----------------------------------|:---------------------------------------------|:-----------------| | <code>فلوتس أصبع</code> | <code>كيت كات شوكلاتة 4 اصابع 36.5 جم</code> | <code>0.0</code> | | <code>بخور عود ند شيخ العرب</code> | <code>بخور العود- اصل العود</code> | <code>0.0</code> | | <code>احمر شفاه Rhode</code> | <code>احمر شفاه - Water Lip Matte</code> | <code>0.0</code> |
• —Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }

Evaluation Dataset

arabic-ecom-data

• —Dataset: arabic-ecom-data
• —Size: 5,021 evaluation samples
• —Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
• —Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 3 tokens</li><li>mean: 7.86 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.19 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
• —Samples: | sentence1 | sentence2 | label | |:------------------------------|:--------------------------------------------------------|:-----------------| | <code>مناديل مطبخ</code> | <code>صابون اواني جودي - 960 مل (الليمون الاخضر)</code> | <code>0.0</code> | | <code>جبنة هابي كاو</code> | <code>هابي كاو جبنة كريمى - 150 غ</code> | <code>1.0</code> | | <code>كريم تايغر للشعر</code> | <code>كريم ازالة شعر - Page Vine</code> | <code>0.0</code> |
• —Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }

Training Hyperparameters

Non-Default Hyperparameters

• —per_device_train_batch_size: 32
• —per_device_eval_batch_size: 32
• —learning_rate: 2e-05
• —num_train_epochs: 2
• —warmup_steps: 0.1
• —fp16: True

All Hyperparameters

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

• —do_predict: False
• —prediction_loss_only: True
• —per_device_train_batch_size: 32
• —per_device_eval_batch_size: 32
• —gradient_accumulation_steps: 1
• —eval_accumulation_steps: None
• —torch_empty_cache_steps: None
• —learning_rate: 2e-05
• —weight_decay: 0.0
• —adam_beta1: 0.9
• —adam_beta2: 0.999
• —adam_epsilon: 1e-08
• —max_grad_norm: 1.0
• —num_train_epochs: 2
• —max_steps: -1
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: None
• —warmup_ratio: None
• —warmup_steps: 0.1
• —log_level: passive
• —log_level_replica: warning
• —log_on_each_node: True
• —logging_nan_inf_filter: True
• —enable_jit_checkpoint: False
• —save_on_each_node: False
• —save_only_model: False
• —restore_callback_states_from_checkpoint: False
• —use_cpu: False
• —seed: 42
• —data_seed: None
• —bf16: False
• —fp16: True
• —bf16_full_eval: False
• —fp16_full_eval: False
• —tf32: None
• —local_rank: -1
• —ddp_backend: None
• —debug: []
• —dataloader_drop_last: False
• —dataloader_num_workers: 0
• —dataloader_prefetch_factor: None
• —disable_tqdm: False
• —remove_unused_columns: True
• —label_names: None
• —load_best_model_at_end: False
• —ignore_data_skip: False
• —fsdp: []
• —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —optim_args: None
• —group_by_length: False
• —length_column_name: length
• —project: huggingface
• —trackio_space_id: trackio
• —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
• —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_for_metrics: []
• —eval_do_concat_batches: True
• —auto_find_batch_size: False
• —full_determinism: False
• —ddp_timeout: 1800
• —torch_compile: False
• —torch_compile_backend: None
• —torch_compile_mode: None
• —include_num_input_tokens_seen: no
• —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: True
• —use_cache: False
• —prompts: None
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: proportional
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

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

</details>

Training Time

• —Training: 21.4 minutes

Framework Versions

• —Python: 3.12.13
• —Sentence Transformers: 5.4.1
• —Transformers: 5.0.0
• —PyTorch: 2.10.0+cu128
• —Accelerate: 1.13.0
• —Datasets: 5.0.0
• —Tokenizers: 0.22.2

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",
}

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