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@@ -38,3 +38,4 @@ checkpoint-400/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 checkpoint-600/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 checkpoint-800/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 checkpoint-1000/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+checkpoint-1200/tokenizer.json filter=lfs diff=lfs merge=lfs -text

checkpoint-1200/1_Pooling/config.json

@@ -0,0 +1,5 @@
+{
+    "embedding_dimension": 1024,
+    "pooling_mode": "lasttoken",
+    "include_prompt": true
+}

checkpoint-1200/README.md

@@ -0,0 +1,515 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:342061
+- loss:CachedMultipleNegativesRankingLoss
+base_model: prestoai/qwen3-embedding-0.6b-arabic-ecom
+widget:
+- source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the
+    product that best matches it
+
+    Query: نسونكس Spray'
+  sentences:
+  - Nasonex - Nasal Spray
+  - كابل شحن مايكرو XKIN - 2.4A
+  - حلوى الشوكولاتة - Choco Lapki
+- source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the
+    product that best matches it
+
+    Query: مكرونة رقم 42'
+  sentences:
+  - بنطلون رجالي - 0112
+  - حقيبة حزام خصر - 4862
+  - مكرونة الجيد معكوفة رقم 42 - 500 غ
+- source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the
+    product that best matches it
+
+    Query: سباغيتي'
+  sentences:
+  - ملعب كرة قدم - DD18
+  - مكرونة معكوفة - Favelli
+  - مكرونة سباغيتي - Favelli
+- source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the
+    product that best matches it
+
+    Query: جبنة هواء'
+  sentences:
+  - جبنة - ابو الولد
+  - جبنة - Hawaa
+  - كاني طعام كلاب البالغين دجاج - 3 ك
+- source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the
+    product that best matches it
+
+    Query: شاحن تايب سي للسيارة'
+  sentences:
+  - شاحن سيارة قرين ليون مدخلين 36 وات مع كابل تايب سي - CBK
+  - صوص المكرونة هاينز - 365 غ
+  - بسكويت جولون بدون سكر شكلاتة ساندوتش
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on prestoai/qwen3-embedding-0.6b-arabic-ecom
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [prestoai/qwen3-embedding-0.6b-arabic-ecom](https://huggingface.co/prestoai/qwen3-embedding-0.6b-arabic-ecom) on the pairs_with_negatives and positives datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [prestoai/qwen3-embedding-0.6b-arabic-ecom](https://huggingface.co/prestoai/qwen3-embedding-0.6b-arabic-ecom) <!-- at revision 80f273fd53c6644d65e14a2ac1fbf74b8c924097 -->
+- **Maximum Sequence Length:** 128 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Supported Modality:** Text
+- **Training Datasets:**
+    - pairs_with_negatives
+    - positives
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### 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': 'Qwen3Model'})
+  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'lasttoken', 'include_prompt': True})
+  (2): Normalize({})
+)
+```
+
+## 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 SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+queries = [
+    'Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it\nQuery: شاحن تايب سي للسيارة',
+]
+documents = [
+    'شاحن سيارة قرين ليون مدخلين 36 وات مع كابل تايب سي - CBK',
+    'بسكويت جولون بدون سكر شكلاتة ساندوتش',
+    'صوص المكرونة هاينز - 365 غ',
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# [1, 1024] [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+# tensor([[ 0.6392, -0.0423, -0.0569]])
+```
+<!--
+### 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 Datasets
+
+#### pairs_with_negatives
+
+* Dataset: pairs_with_negatives
+* Size: 124,261 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                          | negative                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 23 tokens</li><li>mean: 29.44 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.95 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 14.87 tokens</li><li>max: 40 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                | positive                                                        | negative                                                           |
+  |:--------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|:-------------------------------------------------------------------|
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: واقي للوجه دهني</code>   | <code>Anthelios Oil Control (Dry Touch) - La Roche Posay</code> | <code>Anthelios Invisible Mist (Dry Touch) - La Roche Posay</code> |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: تن منارة زليتن</code>    | <code>تن منارة زليتن بزيت دوار الشمس - 160 غ</code>             | <code>تن فاني بزيت دوار الشمس - 160 غ</code>                       |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: فانتا زجاجة صغيرة</code> | <code>مشروب فانتا برتقال زجاجة - 330 مل</code>                  | <code>مشروب فانتا - 1 ل (برتقال)</code>                            |
+* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "mini_batch_size": 8,
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+#### positives
+
+* Dataset: positives
+* Size: 217,800 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 23 tokens</li><li>mean: 29.61 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 13.81 tokens</li><li>max: 41 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                         | positive                                   |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------|
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: نحب جبنة القرية</code>            | <code>ميرسين جبنة القرية 200 جم</code>     |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: كابل شحن مايكرو Moxom A2.4</code> | <code>كابل شحن مايكرو Moxom -  A2.4</code> |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: dry idea</code>                   | <code>Dry idea (powder fresh)</code>       |
+* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "mini_batch_size": 8,
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+### Evaluation Datasets
+
+#### pairs_with_negatives
+
+* Dataset: pairs_with_negatives
+* Size: 1,256 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                          | negative                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 23 tokens</li><li>mean: 29.56 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 16.04 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.05 tokens</li><li>max: 33 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                               | positive                                               | negative                              |
+  |:-------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:--------------------------------------|
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: هيبوتك بوزن</code>      | <code>عطر Hypnotic Poison - PERFECTO COLLECTION</code> | <code>عطر Poison Girl - Dior</code>   |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: شاحن مايكرو 2.4A</code> | <code>شحن مايكرو Smila - 2.4A</code>                   | <code>شحن تايب سي Smila - 2.4A</code> |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: تمر صعيدي</code>        | <code>تمر صعيدي مشفوط</code>                           | <code>تمر قصيم مشفوط</code>           |
+* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "mini_batch_size": 8,
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+#### positives
+
+* Dataset: positives
+* Size: 2,200 evaluation samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            |
+  | details | <ul><li>min: 24 tokens</li><li>mean: 29.27 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.61 tokens</li><li>max: 44 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                           | positive                                                     |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------|
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: حاملة أدوات القطط</code>            | <code>حاملة أدوات القطة - AA04</code>                        |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: كريم شمس أطفال</code>               | <code>واقي شمس كريمي - Chicco</code>                         |
+  | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: كانديسارتان هيدروكلوروثيازيد</code> | <code>Candesartan and Hydrochlorothiazide 16mg/12.5mg</code> |
+* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "mini_batch_size": 8,
+      "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`: 32
+- `learning_rate`: 0.0001
+- `num_train_epochs`: 1
+- `warmup_steps`: 0.05
+- `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`: 8
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 0.0001
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_ratio`: None
+- `warmup_steps`: 0.05
+- `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
+| Epoch  | Step | Training Loss | pairs with negatives loss | positives loss |
+|:------:|:----:|:-------------:|:-------------------------:|:--------------:|
+| 0.0023 | 25   | 0.4772        | -                         | -              |
+| 0.0047 | 50   | 0.4593        | -                         | -              |
+| 0.0070 | 75   | 0.3945        | -                         | -              |
+| 0.0094 | 100  | 0.3752        | -                         | -              |
+| 0.0117 | 125  | 0.4048        | -                         | -              |
+| 0.0140 | 150  | 0.4563        | -                         | -              |
+| 0.0164 | 175  | 0.3492        | -                         | -              |
+| 0.0187 | 200  | 0.4171        | 0.3915                    | 0.1481         |
+| 0.0210 | 225  | 0.4297        | -                         | -              |
+| 0.0234 | 250  | 0.4365        | -                         | -              |
+| 0.0257 | 275  | 0.4344        | -                         | -              |
+| 0.0281 | 300  | 0.4184        | -                         | -              |
+| 0.0304 | 325  | 0.4198        | -                         | -              |
+| 0.0327 | 350  | 0.4293        | -                         | -              |
+| 0.0351 | 375  | 0.4759        | -                         | -              |
+| 0.0374 | 400  | 0.3312        | 0.3695                    | 0.1180         |
+| 0.0398 | 425  | 0.3887        | -                         | -              |
+| 0.0421 | 450  | 0.4402        | -                         | -              |
+| 0.0444 | 475  | 0.4105        | -                         | -              |
+| 0.0468 | 500  | 0.3923        | -                         | -              |
+| 0.0491 | 525  | 0.3163        | -                         | -              |
+| 0.0514 | 550  | 0.3565        | -                         | -              |
+| 0.0538 | 575  | 0.3707        | -                         | -              |
+| 0.0561 | 600  | 0.3008        | 0.3388                    | 0.1086         |
+| 0.0585 | 625  | 0.3594        | -                         | -              |
+| 0.0608 | 650  | 0.3936        | -                         | -              |
+| 0.0631 | 675  | 0.3207        | -                         | -              |
+| 0.0655 | 700  | 0.3371        | -                         | -              |
+| 0.0678 | 725  | 0.3385        | -                         | -              |
+| 0.0702 | 750  | 0.2718        | -                         | -              |
+| 0.0725 | 775  | 0.4429        | -                         | -              |
+| 0.0748 | 800  | 0.2684        | 0.3453                    | 0.1043         |
+| 0.0772 | 825  | 0.2539        | -                         | -              |
+| 0.0795 | 850  | 0.3239        | -                         | -              |
+| 0.0818 | 875  | 0.2944        | -                         | -              |
+| 0.0842 | 900  | 0.3067        | -                         | -              |
+| 0.0865 | 925  | 0.3113        | -                         | -              |
+| 0.0889 | 950  | 0.3387        | -                         | -              |
+| 0.0912 | 975  | 0.2735        | -                         | -              |
+| 0.0935 | 1000 | 0.2985        | 0.3211                    | 0.0891         |
+| 0.0959 | 1025 | 0.3553        | -                         | -              |
+| 0.0982 | 1050 | 0.2568        | -                         | -              |
+| 0.1006 | 1075 | 0.3447        | -                         | -              |
+| 0.1029 | 1100 | 0.3239        | -                         | -              |
+| 0.1052 | 1125 | 0.3015        | -                         | -              |
+| 0.1076 | 1150 | 0.3865        | -                         | -              |
+| 0.1099 | 1175 | 0.2982        | -                         | -              |
+| 0.1122 | 1200 | 0.3105        | 0.3232                    | 0.0829         |
+
+
+### Training Time
+- **Training**: 1.3 hours
+
+### 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",
+}
+```
+
+#### CachedMultipleNegativesRankingLoss
+```bibtex
+@misc{gao2021scaling,
+    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
+    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
+    year={2021},
+    eprint={2101.06983},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## 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.*
+-->

checkpoint-1200/adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "prestoai/qwen3-embedding-0.6b-arabic-ecom",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "gate_proj",
+    "down_proj",
+    "o_proj",
+    "k_proj",
+    "up_proj",
+    "q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "FEATURE_EXTRACTION",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
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checkpoint-1200/adapter_model.safetensors

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checkpoint-1200/chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
+                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

checkpoint-1200/config_sentence_transformers.json

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+{
+  "__version__": {
+    "pytorch": "2.10.0+cu128",
+    "sentence_transformers": "5.4.1",
+    "transformers": "5.0.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "document": "",
+    "query": "Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it\nQuery: "
+  },
+  "similarity_fn_name": "cosine"
+}

checkpoint-1200/modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
+  }
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+size 1465

checkpoint-1200/sentence_bert_config.json

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+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

checkpoint-1200/tokenizer.json

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checkpoint-1200/tokenizer_config.json

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+  "backend": "tokenizers",
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+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 128,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
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