Add new CrossEncoder model

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.gitattributes +1 -0
README.md +496 -0
config.json +35 -0
config_sentence_transformers.json +11 -0
model.safetensors +3 -0
modules.json +8 -0
sentence_bert_config.json +10 -0
tokenizer.json +3 -0
tokenizer_config.json +14 -0

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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md ADDED Viewed

	@@ -0,0 +1,496 @@

+---
+tags:
+- sentence-transformers
+- cross-encoder
+- reranker
+- generated_from_trainer
+- dataset_size:246013
+- loss:BinaryCrossEntropyLoss
+base_model: cross-encoder/mmarco-mMiniLMv2-L12-H384-v1
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+---
+# CrossEncoder based on cross-encoder/mmarco-mMiniLMv2-L12-H384-v1
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/mmarco-mMiniLMv2-L12-H384-v1](https://huggingface.co/cross-encoder/mmarco-mMiniLMv2-L12-H384-v1) on the arabic-ecom-data dataset using the [sentence-transformers](https://www.SBERT.net) 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](https://huggingface.co/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](https://sbert.net)
+- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
+### 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:
+```bash
+pip install -U sentence-transformers
+```
+Then you can load this model and run inference.
+```python
+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': ...}, ...]
+```
+<!--
+### 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
+#### 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>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
+  ```json
+  {
+      "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>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
+  ```json
+  {
+      "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`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `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>
+| Epoch  | Step  | Training Loss | Validation Loss |
+|:------:|:-----:|:-------------:|:---------------:|
+| 0.0130 | 100   | 0.8919        | -               |
+| 0.0260 | 200   | 0.6599        | -               |
+| 0.0390 | 300   | 0.5613        | -               |
+| 0.0520 | 400   | 0.5168        | -               |
+| 0.0650 | 500   | 0.5278        | 0.4916          |
+| 0.0780 | 600   | 0.5182        | -               |
+| 0.0911 | 700   | 0.4833        | -               |
+| 0.1041 | 800   | 0.4863        | -               |
+| 0.1171 | 900   | 0.5011        | -               |
+| 0.1301 | 1000  | 0.4740        | 0.4477          |
+| 0.1431 | 1100  | 0.4480        | -               |
+| 0.1561 | 1200  | 0.4536        | -               |
+| 0.1691 | 1300  | 0.4604        | -               |
+| 0.1821 | 1400  | 0.4704        | -               |
+| 0.1951 | 1500  | 0.4514        | 0.4282          |
+| 0.2081 | 1600  | 0.4358        | -               |
+| 0.2211 | 1700  | 0.4472        | -               |
+| 0.2341 | 1800  | 0.4382        | -               |
+| 0.2471 | 1900  | 0.4524        | -               |
+| 0.2601 | 2000  | 0.4368        | 0.4112          |
+| 0.2732 | 2100  | 0.4272        | -               |
+| 0.2862 | 2200  | 0.4280        | -               |
+| 0.2992 | 2300  | 0.4276        | -               |
+| 0.3122 | 2400  | 0.4067        | -               |
+| 0.3252 | 2500  | 0.4260        | 0.4026          |
+| 0.3382 | 2600  | 0.4321        | -               |
+| 0.3512 | 2700  | 0.4333        | -               |
+| 0.3642 | 2800  | 0.4246        | -               |
+| 0.3772 | 2900  | 0.4304        | -               |
+| 0.3902 | 3000  | 0.4237        | 0.3938          |
+| 0.4032 | 3100  | 0.4181        | -               |
+| 0.4162 | 3200  | 0.4224        | -               |
+| 0.4292 | 3300  | 0.4096        | -               |
+| 0.4422 | 3400  | 0.4069        | -               |
+| 0.4553 | 3500  | 0.4045        | 0.3963          |
+| 0.4683 | 3600  | 0.4164        | -               |
+| 0.4813 | 3700  | 0.3996        | -               |
+| 0.4943 | 3800  | 0.4053        | -               |
+| 0.5073 | 3900  | 0.3853        | -               |
+| 0.5203 | 4000  | 0.4035        | 0.3818          |
+| 0.5333 | 4100  | 0.4043        | -               |
+| 0.5463 | 4200  | 0.3914        | -               |
+| 0.5593 | 4300  | 0.4022        | -               |
+| 0.5723 | 4400  | 0.3949        | -               |
+| 0.5853 | 4500  | 0.4094        | 0.3821          |
+| 0.5983 | 4600  | 0.3782        | -               |
+| 0.6113 | 4700  | 0.3908        | -               |
+| 0.6243 | 4800  | 0.3944        | -               |
+| 0.6374 | 4900  | 0.4112        | -               |
+| 0.6504 | 5000  | 0.4077        | 0.3676          |
+| 0.6634 | 5100  | 0.4034        | -               |
+| 0.6764 | 5200  | 0.3958        | -               |
+| 0.6894 | 5300  | 0.3988        | -               |
+| 0.7024 | 5400  | 0.3835        | -               |
+| 0.7154 | 5500  | 0.4065        | 0.3680          |
+| 0.7284 | 5600  | 0.3910        | -               |
+| 0.7414 | 5700  | 0.3959        | -               |
+| 0.7544 | 5800  | 0.4005        | -               |
+| 0.7674 | 5900  | 0.3967        | -               |
+| 0.7804 | 6000  | 0.3947        | 0.3734          |
+| 0.7934 | 6100  | 0.3916        | -               |
+| 0.8065 | 6200  | 0.4023        | -               |
+| 0.8195 | 6300  | 0.3869        | -               |
+| 0.8325 | 6400  | 0.3821        | -               |
+| 0.8455 | 6500  | 0.3845        | 0.3716          |
+| 0.8585 | 6600  | 0.3637        | -               |
+| 0.8715 | 6700  | 0.3828        | -               |
+| 0.8845 | 6800  | 0.3703        | -               |
+| 0.8975 | 6900  | 0.3962        | -               |
+| 0.9105 | 7000  | 0.3880        | 0.3592          |
+| 0.9235 | 7100  | 0.3846        | -               |
+| 0.9365 | 7200  | 0.3722        | -               |
+| 0.9495 | 7300  | 0.3946        | -               |
+| 0.9625 | 7400  | 0.3779        | -               |
+| 0.9755 | 7500  | 0.3957        | 0.3550          |
+| 0.9886 | 7600  | 0.3763        | -               |
+| 1.0016 | 7700  | 0.3732        | -               |
+| 1.0146 | 7800  | 0.3763        | -               |
+| 1.0276 | 7900  | 0.3713        | -               |
+| 1.0406 | 8000  | 0.3594        | 0.3597          |
+| 1.0536 | 8100  | 0.3510        | -               |
+| 1.0666 | 8200  | 0.3738        | -               |
+| 1.0796 | 8300  | 0.3554        | -               |
+| 1.0926 | 8400  | 0.3524        | -               |
+| 1.1056 | 8500  | 0.3507        | 0.3577          |
+| 1.1186 | 8600  | 0.3483        | -               |
+| 1.1316 | 8700  | 0.3692        | -               |
+| 1.1446 | 8800  | 0.3676        | -               |
+| 1.1576 | 8900  | 0.3484        | -               |
+| 1.1707 | 9000  | 0.3859        | 0.3502          |
+| 1.1837 | 9100  | 0.3590        | -               |
+| 1.1967 | 9200  | 0.3746        | -               |
+| 1.2097 | 9300  | 0.3559        | -               |
+| 1.2227 | 9400  | 0.3631        | -               |
+| 1.2357 | 9500  | 0.3500        | 0.3685          |
+| 1.2487 | 9600  | 0.3496        | -               |
+| 1.2617 | 9700  | 0.3803        | -               |
+| 1.2747 | 9800  | 0.3442        | -               |
+| 1.2877 | 9900  | 0.3503        | -               |
+| 1.3007 | 10000 | 0.3636        | 0.3504          |
+| 1.3137 | 10100 | 0.3479        | -               |
+| 1.3267 | 10200 | 0.3768        | -               |
+| 1.3398 | 10300 | 0.3501        | -               |
+| 1.3528 | 10400 | 0.3563        | -               |
+| 1.3658 | 10500 | 0.3551        | 0.3515          |
+| 1.3788 | 10600 | 0.3645        | -               |
+| 1.3918 | 10700 | 0.3466        | -               |
+| 1.4048 | 10800 | 0.3622        | -               |
+| 1.4178 | 10900 | 0.3535        | -               |
+| 1.4308 | 11000 | 0.3708        | 0.3452          |
+| 1.4438 | 11100 | 0.3484        | -               |
+| 1.4568 | 11200 | 0.3593        | -               |
+| 1.4698 | 11300 | 0.3554        | -               |
+| 1.4828 | 11400 | 0.3362        | -               |
+| 1.4958 | 11500 | 0.3707        | 0.3458          |
+| 1.5088 | 11600 | 0.3559        | -               |
+| 1.5219 | 11700 | 0.3501        | -               |
+| 1.5349 | 11800 | 0.3771        | -               |
+| 1.5479 | 11900 | 0.3586        | -               |
+| 1.5609 | 12000 | 0.3462        | 0.3478          |
+| 1.5739 | 12100 | 0.3448        | -               |
+| 1.5869 | 12200 | 0.3516        | -               |
+| 1.5999 | 12300 | 0.3582        | -               |
+| 1.6129 | 12400 | 0.3621        | -               |
+| 1.6259 | 12500 | 0.3724        | 0.3436          |
+| 1.6389 | 12600 | 0.3598        | -               |
+| 1.6519 | 12700 | 0.3616        | -               |
+| 1.6649 | 12800 | 0.3537        | -               |
+| 1.6779 | 12900 | 0.3462        | -               |
+| 1.6909 | 13000 | 0.3675        | 0.3443          |
+| 1.7040 | 13100 | 0.3506        | -               |
+| 1.7170 | 13200 | 0.3389        | -               |
+| 1.7300 | 13300 | 0.3454        | -               |
+| 1.7430 | 13400 | 0.3588        | -               |
+| 1.7560 | 13500 | 0.3521        | 0.3427          |
+| 1.7690 | 13600 | 0.3462        | -               |
+| 1.7820 | 13700 | 0.3513        | -               |
+| 1.7950 | 13800 | 0.3484        | -               |
+| 1.8080 | 13900 | 0.3522        | -               |
+| 1.8210 | 14000 | 0.3426        | 0.3447          |
+| 1.8340 | 14100 | 0.3497        | -               |
+| 1.8470 | 14200 | 0.3464        | -               |
+| 1.8600 | 14300 | 0.3427        | -               |
+| 1.8730 | 14400 | 0.3422        | -               |
+| 1.8861 | 14500 | 0.3398        | 0.3447          |
+| 1.8991 | 14600 | 0.3487        | -               |
+| 1.9121 | 14700 | 0.3608        | -               |
+| 1.9251 | 14800 | 0.3515        | -               |
+| 1.9381 | 14900 | 0.3456        | -               |
+| 1.9511 | 15000 | 0.3499        | 0.3445          |
+| 1.9641 | 15100 | 0.3404        | -               |
+| 1.9771 | 15200 | 0.3482        | -               |
+| 1.9901 | 15300 | 0.3464        | -               |
+</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
+```bibtex
+@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",
+}
+```
+<!--
+## Glossary
+*Clearly define terms in order to be accessible across audiences.*
+-->
+<!--
+## Model Card Authors
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+<!--
+## Model Card Contact
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json ADDED Viewed

	@@ -0,0 +1,35 @@

+{
+  "add_cross_attention": false,
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
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+  "tokenizer_class": "XLMRobertaTokenizer",
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