Upload folder using huggingface_hub

.gitattributes

@@ -81,3 +81,4 @@ checkpoint-9000/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 checkpoint-9200/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 checkpoint-9400/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 checkpoint-9600/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+checkpoint-9800/tokenizer.json filter=lfs diff=lfs merge=lfs -text

checkpoint-9800/1_Pooling/config.json

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

checkpoint-9800/README.md

@@ -0,0 +1,862 @@
+---
+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([[ 0.6852, -0.0607, -0.0515]])
+```
+<!--
+### 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
+<details><summary>Click to expand</summary>
+
+| 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         |
+| 0.1146 | 1225 | 0.2964        | -                         | -              |
+| 0.1169 | 1250 | 0.2417        | -                         | -              |
+| 0.1193 | 1275 | 0.2686        | -                         | -              |
+| 0.1216 | 1300 | 0.2932        | -                         | -              |
+| 0.1239 | 1325 | 0.2383        | -                         | -              |
+| 0.1263 | 1350 | 0.3108        | -                         | -              |
+| 0.1286 | 1375 | 0.3216        | -                         | -              |
+| 0.1310 | 1400 | 0.2083        | 0.3091                    | 0.0894         |
+| 0.1333 | 1425 | 0.2933        | -                         | -              |
+| 0.1356 | 1450 | 0.2038        | -                         | -              |
+| 0.1380 | 1475 | 0.2515        | -                         | -              |
+| 0.1403 | 1500 | 0.2643        | -                         | -              |
+| 0.1426 | 1525 | 0.2484        | -                         | -              |
+| 0.1450 | 1550 | 0.3216        | -                         | -              |
+| 0.1473 | 1575 | 0.3265        | -                         | -              |
+| 0.1497 | 1600 | 0.2626        | 0.3166                    | 0.0775         |
+| 0.1520 | 1625 | 0.2811        | -                         | -              |
+| 0.1543 | 1650 | 0.2792        | -                         | -              |
+| 0.1567 | 1675 | 0.2888        | -                         | -              |
+| 0.1590 | 1700 | 0.3243        | -                         | -              |
+| 0.1614 | 1725 | 0.2318        | -                         | -              |
+| 0.1637 | 1750 | 0.2943        | -                         | -              |
+| 0.1660 | 1775 | 0.2494        | -                         | -              |
+| 0.1684 | 1800 | 0.3478        | 0.3113                    | 0.0751         |
+| 0.1707 | 1825 | 0.3265        | -                         | -              |
+| 0.1730 | 1850 | 0.2933        | -                         | -              |
+| 0.1754 | 1875 | 0.2671        | -                         | -              |
+| 0.1777 | 1900 | 0.2927        | -                         | -              |
+| 0.1801 | 1925 | 0.2939        | -                         | -              |
+| 0.1824 | 1950 | 0.2356        | -                         | -              |
+| 0.1847 | 1975 | 0.2413        | -                         | -              |
+| 0.1871 | 2000 | 0.2026        | 0.2921                    | 0.0650         |
+| 0.1894 | 2025 | 0.2663        | -                         | -              |
+| 0.1918 | 2050 | 0.2438        | -                         | -              |
+| 0.1941 | 2075 | 0.2321        | -                         | -              |
+| 0.1964 | 2100 | 0.2482        | -                         | -              |
+| 0.1988 | 2125 | 0.3000        | -                         | -              |
+| 0.2011 | 2150 | 0.1990        | -                         | -              |
+| 0.2034 | 2175 | 0.2393        | -                         | -              |
+| 0.2058 | 2200 | 0.2370        | 0.2844                    | 0.0670         |
+| 0.2081 | 2225 | 0.2131        | -                         | -              |
+| 0.2105 | 2250 | 0.2548        | -                         | -              |
+| 0.2128 | 2275 | 0.3016        | -                         | -              |
+| 0.2151 | 2300 | 0.1959        | -                         | -              |
+| 0.2175 | 2325 | 0.2604        | -                         | -              |
+| 0.2198 | 2350 | 0.3141        | -                         | -              |
+| 0.2221 | 2375 | 0.2729        | -                         | -              |
+| 0.2245 | 2400 | 0.2492        | 0.2854                    | 0.0640         |
+| 0.2268 | 2425 | 0.2326        | -                         | -              |
+| 0.2292 | 2450 | 0.2850        | -                         | -              |
+| 0.2315 | 2475 | 0.2393        | -                         | -              |
+| 0.2338 | 2500 | 0.2748        | -                         | -              |
+| 0.2362 | 2525 | 0.2289        | -                         | -              |
+| 0.2385 | 2550 | 0.2486        | -                         | -              |
+| 0.2409 | 2575 | 0.2846        | -                         | -              |
+| 0.2432 | 2600 | 0.2027        | 0.2798                    | 0.0586         |
+| 0.2455 | 2625 | 0.2336        | -                         | -              |
+| 0.2479 | 2650 | 0.2207        | -                         | -              |
+| 0.2502 | 2675 | 0.2357        | -                         | -              |
+| 0.2525 | 2700 | 0.2132        | -                         | -              |
+| 0.2549 | 2725 | 0.2152        | -                         | -              |
+| 0.2572 | 2750 | 0.2046        | -                         | -              |
+| 0.2596 | 2775 | 0.1824        | -                         | -              |
+| 0.2619 | 2800 | 0.2406        | 0.2775                    | 0.0624         |
+| 0.2642 | 2825 | 0.2240        | -                         | -              |
+| 0.2666 | 2850 | 0.2538        | -                         | -              |
+| 0.2689 | 2875 | 0.1901        | -                         | -              |
+| 0.2713 | 2900 | 0.2792        | -                         | -              |
+| 0.2736 | 2925 | 0.2489        | -                         | -              |
+| 0.2759 | 2950 | 0.2371        | -                         | -              |
+| 0.2783 | 2975 | 0.2170        | -                         | -              |
+| 0.2806 | 3000 | 0.2408        | 0.2762                    | 0.0570         |
+| 0.2829 | 3025 | 0.1543        | -                         | -              |
+| 0.2853 | 3050 | 0.2858        | -                         | -              |
+| 0.2876 | 3075 | 0.2290        | -                         | -              |
+| 0.2900 | 3100 | 0.3003        | -                         | -              |
+| 0.2923 | 3125 | 0.2143        | -                         | -              |
+| 0.2946 | 3150 | 0.2486        | -                         | -              |
+| 0.2970 | 3175 | 0.2412        | -                         | -              |
+| 0.2993 | 3200 | 0.2683        | 0.2755                    | 0.0600         |
+| 0.3017 | 3225 | 0.2805        | -                         | -              |
+| 0.3040 | 3250 | 0.1677        | -                         | -              |
+| 0.3063 | 3275 | 0.2121        | -                         | -              |
+| 0.3087 | 3300 | 0.1980        | -                         | -              |
+| 0.3110 | 3325 | 0.2685        | -                         | -              |
+| 0.3133 | 3350 | 0.2513        | -                         | -              |
+| 0.3157 | 3375 | 0.2237        | -                         | -              |
+| 0.3180 | 3400 | 0.2345        | 0.2724                    | 0.0598         |
+| 0.3204 | 3425 | 0.1962        | -                         | -              |
+| 0.3227 | 3450 | 0.2863        | -                         | -              |
+| 0.3250 | 3475 | 0.2069        | -                         | -              |
+| 0.3274 | 3500 | 0.2614        | -                         | -              |
+| 0.3297 | 3525 | 0.2608        | -                         | -              |
+| 0.3321 | 3550 | 0.1719        | -                         | -              |
+| 0.3344 | 3575 | 0.1922        | -                         | -              |
+| 0.3367 | 3600 | 0.2148        | 0.2833                    | 0.0574         |
+| 0.3391 | 3625 | 0.2505        | -                         | -              |
+| 0.3414 | 3650 | 0.2786        | -                         | -              |
+| 0.3437 | 3675 | 0.2462        | -                         | -              |
+| 0.3461 | 3700 | 0.2319        | -                         | -              |
+| 0.3484 | 3725 | 0.2415        | -                         | -              |
+| 0.3508 | 3750 | 0.2363        | -                         | -              |
+| 0.3531 | 3775 | 0.1833        | -                         | -              |
+| 0.3554 | 3800 | 0.2266        | 0.2764                    | 0.0558         |
+| 0.3578 | 3825 | 0.1910        | -                         | -              |
+| 0.3601 | 3850 | 0.2883        | -                         | -              |
+| 0.3625 | 3875 | 0.1867        | -                         | -              |
+| 0.3648 | 3900 | 0.2211        | -                         | -              |
+| 0.3671 | 3925 | 0.2308        | -                         | -              |
+| 0.3695 | 3950 | 0.2339        | -                         | -              |
+| 0.3718 | 3975 | 0.2290        | -                         | -              |
+| 0.3741 | 4000 | 0.2557        | 0.2681                    | 0.0557         |
+| 0.3765 | 4025 | 0.2158        | -                         | -              |
+| 0.3788 | 4050 | 0.2279        | -                         | -              |
+| 0.3812 | 4075 | 0.2348        | -                         | -              |
+| 0.3835 | 4100 | 0.3228        | -                         | -              |
+| 0.3858 | 4125 | 0.2015        | -                         | -              |
+| 0.3882 | 4150 | 0.1773        | -                         | -              |
+| 0.3905 | 4175 | 0.2194        | -                         | -              |
+| 0.3929 | 4200 | 0.2076        | 0.2610                    | 0.0545         |
+| 0.3952 | 4225 | 0.2413        | -                         | -              |
+| 0.3975 | 4250 | 0.2106        | -                         | -              |
+| 0.3999 | 4275 | 0.2011        | -                         | -              |
+| 0.4022 | 4300 | 0.2752        | -                         | -              |
+| 0.4045 | 4325 | 0.2190        | -                         | -              |
+| 0.4069 | 4350 | 0.2234        | -                         | -              |
+| 0.4092 | 4375 | 0.2648        | -                         | -              |
+| 0.4116 | 4400 | 0.2325        | 0.2550                    | 0.0508         |
+| 0.4139 | 4425 | 0.3020        | -                         | -              |
+| 0.4162 | 4450 | 0.2301        | -                         | -              |
+| 0.4186 | 4475 | 0.1998        | -                         | -              |
+| 0.4209 | 4500 | 0.2532        | -                         | -              |
+| 0.4233 | 4525 | 0.2002        | -                         | -              |
+| 0.4256 | 4550 | 0.2243        | -                         | -              |
+| 0.4279 | 4575 | 0.2274        | -                         | -              |
+| 0.4303 | 4600 | 0.1878        | 0.2571                    | 0.0476         |
+| 0.4326 | 4625 | 0.2324        | -                         | -              |
+| 0.4349 | 4650 | 0.1931        | -                         | -              |
+| 0.4373 | 4675 | 0.2420        | -                         | -              |
+| 0.4396 | 4700 | 0.2707        | -                         | -              |
+| 0.4420 | 4725 | 0.2175        | -                         | -              |
+| 0.4443 | 4750 | 0.2280        | -                         | -              |
+| 0.4466 | 4775 | 0.2171        | -                         | -              |
+| 0.4490 | 4800 | 0.2313        | 0.2441                    | 0.0505         |
+| 0.4513 | 4825 | 0.2048        | -                         | -              |
+| 0.4537 | 4850 | 0.2170        | -                         | -              |
+| 0.4560 | 4875 | 0.2318        | -                         | -              |
+| 0.4583 | 4900 | 0.2193        | -                         | -              |
+| 0.4607 | 4925 | 0.2338        | -                         | -              |
+| 0.4630 | 4950 | 0.2273        | -                         | -              |
+| 0.4653 | 4975 | 0.2223        | -                         | -              |
+| 0.4677 | 5000 | 0.2221        | 0.2421                    | 0.0469         |
+| 0.4700 | 5025 | 0.1880        | -                         | -              |
+| 0.4724 | 5050 | 0.2160        | -                         | -              |
+| 0.4747 | 5075 | 0.2001        | -                         | -              |
+| 0.4770 | 5100 | 0.1970        | -                         | -              |
+| 0.4794 | 5125 | 0.1892        | -                         | -              |
+| 0.4817 | 5150 | 0.2205        | -                         | -              |
+| 0.4841 | 5175 | 0.1817        | -                         | -              |
+| 0.4864 | 5200 | 0.1862        | 0.2394                    | 0.0467         |
+| 0.4887 | 5225 | 0.1886        | -                         | -              |
+| 0.4911 | 5250 | 0.2649        | -                         | -              |
+| 0.4934 | 5275 | 0.2546        | -                         | -              |
+| 0.4957 | 5300 | 0.2039        | -                         | -              |
+| 0.4981 | 5325 | 0.1852        | -                         | -              |
+| 0.5004 | 5350 | 0.2487        | -                         | -              |
+| 0.5028 | 5375 | 0.1723        | -                         | -              |
+| 0.5051 | 5400 | 0.1718        | 0.2431                    | 0.0469         |
+| 0.5074 | 5425 | 0.1905        | -                         | -              |
+| 0.5098 | 5450 | 0.1800        | -                         | -              |
+| 0.5121 | 5475 | 0.1878        | -                         | -              |
+| 0.5145 | 5500 | 0.2351        | -                         | -              |
+| 0.5168 | 5525 | 0.1957        | -                         | -              |
+| 0.5191 | 5550 | 0.2550        | -                         | -              |
+| 0.5215 | 5575 | 0.1785        | -                         | -              |
+| 0.5238 | 5600 | 0.2594        | 0.2368                    | 0.0475         |
+| 0.5261 | 5625 | 0.1563        | -                         | -              |
+| 0.5285 | 5650 | 0.2244        | -                         | -              |
+| 0.5308 | 5675 | 0.2053        | -                         | -              |
+| 0.5332 | 5700 | 0.2576        | -                         | -              |
+| 0.5355 | 5725 | 0.1932        | -                         | -              |
+| 0.5378 | 5750 | 0.2004        | -                         | -              |
+| 0.5402 | 5775 | 0.2284        | -                         | -              |
+| 0.5425 | 5800 | 0.2033        | 0.2334                    | 0.0477         |
+| 0.5449 | 5825 | 0.2142        | -                         | -              |
+| 0.5472 | 5850 | 0.1734        | -                         | -              |
+| 0.5495 | 5875 | 0.1504        | -                         | -              |
+| 0.5519 | 5900 | 0.2569        | -                         | -              |
+| 0.5542 | 5925 | 0.2062        | -                         | -              |
+| 0.5565 | 5950 | 0.2186        | -                         | -              |
+| 0.5589 | 5975 | 0.1993        | -                         | -              |
+| 0.5612 | 6000 | 0.2260        | 0.2353                    | 0.0470         |
+| 0.5636 | 6025 | 0.2689        | -                         | -              |
+| 0.5659 | 6050 | 0.2504        | -                         | -              |
+| 0.5682 | 6075 | 0.1922        | -                         | -              |
+| 0.5706 | 6100 | 0.2256        | -                         | -              |
+| 0.5729 | 6125 | 0.1792        | -                         | -              |
+| 0.5753 | 6150 | 0.2577        | -                         | -              |
+| 0.5776 | 6175 | 0.2530        | -                         | -              |
+| 0.5799 | 6200 | 0.1786        | 0.2393                    | 0.0452         |
+| 0.5823 | 6225 | 0.2169        | -                         | -              |
+| 0.5846 | 6250 | 0.1938        | -                         | -              |
+| 0.5869 | 6275 | 0.1976        | -                         | -              |
+| 0.5893 | 6300 | 0.2118        | -                         | -              |
+| 0.5916 | 6325 | 0.1709        | -                         | -              |
+| 0.5940 | 6350 | 0.1843        | -                         | -              |
+| 0.5963 | 6375 | 0.1393        | -                         | -              |
+| 0.5986 | 6400 | 0.2299        | 0.2363                    | 0.0449         |
+| 0.6010 | 6425 | 0.2455        | -                         | -              |
+| 0.6033 | 6450 | 0.2199        | -                         | -              |
+| 0.6056 | 6475 | 0.1670        | -                         | -              |
+| 0.6080 | 6500 | 0.2010        | -                         | -              |
+| 0.6103 | 6525 | 0.2063        | -                         | -              |
+| 0.6127 | 6550 | 0.2197        | -                         | -              |
+| 0.6150 | 6575 | 0.2464        | -                         | -              |
+| 0.6173 | 6600 | 0.1821        | 0.2366                    | 0.0445         |
+| 0.6197 | 6625 | 0.2183        | -                         | -              |
+| 0.6220 | 6650 | 0.1417        | -                         | -              |
+| 0.6244 | 6675 | 0.2011        | -                         | -              |
+| 0.6267 | 6700 | 0.1892        | -                         | -              |
+| 0.6290 | 6725 | 0.2117        | -                         | -              |
+| 0.6314 | 6750 | 0.1511        | -                         | -              |
+| 0.6337 | 6775 | 0.2090        | -                         | -              |
+| 0.6360 | 6800 | 0.1923        | 0.2392                    | 0.0427         |
+| 0.6384 | 6825 | 0.1810        | -                         | -              |
+| 0.6407 | 6850 | 0.1613        | -                         | -              |
+| 0.6431 | 6875 | 0.2433        | -                         | -              |
+| 0.6454 | 6900 | 0.1593        | -                         | -              |
+| 0.6477 | 6925 | 0.2303        | -                         | -              |
+| 0.6501 | 6950 | 0.1950        | -                         | -              |
+| 0.6524 | 6975 | 0.1894        | -                         | -              |
+| 0.6548 | 7000 | 0.1990        | 0.2314                    | 0.0421         |
+| 0.6571 | 7025 | 0.1881        | -                         | -              |
+| 0.6594 | 7050 | 0.1941        | -                         | -              |
+| 0.6618 | 7075 | 0.1712        | -                         | -              |
+| 0.6641 | 7100 | 0.2398        | -                         | -              |
+| 0.6664 | 7125 | 0.2113        | -                         | -              |
+| 0.6688 | 7150 | 0.1361        | -                         | -              |
+| 0.6711 | 7175 | 0.1488        | -                         | -              |
+| 0.6735 | 7200 | 0.2518        | 0.2338                    | 0.0429         |
+| 0.6758 | 7225 | 0.2029        | -                         | -              |
+| 0.6781 | 7250 | 0.2350        | -                         | -              |
+| 0.6805 | 7275 | 0.2359        | -                         | -              |
+| 0.6828 | 7300 | 0.2324        | -                         | -              |
+| 0.6852 | 7325 | 0.1804        | -                         | -              |
+| 0.6875 | 7350 | 0.2344        | -                         | -              |
+| 0.6898 | 7375 | 0.1658        | -                         | -              |
+| 0.6922 | 7400 | 0.2431        | 0.2269                    | 0.0437         |
+| 0.6945 | 7425 | 0.1419        | -                         | -              |
+| 0.6968 | 7450 | 0.2092        | -                         | -              |
+| 0.6992 | 7475 | 0.2497        | -                         | -              |
+| 0.7015 | 7500 | 0.1736        | -                         | -              |
+| 0.7039 | 7525 | 0.1469        | -                         | -              |
+| 0.7062 | 7550 | 0.1899        | -                         | -              |
+| 0.7085 | 7575 | 0.1854        | -                         | -              |
+| 0.7109 | 7600 | 0.2064        | 0.2261                    | 0.0433         |
+| 0.7132 | 7625 | 0.1294        | -                         | -              |
+| 0.7156 | 7650 | 0.1601        | -                         | -              |
+| 0.7179 | 7675 | 0.1663        | -                         | -              |
+| 0.7202 | 7700 | 0.1967        | -                         | -              |
+| 0.7226 | 7725 | 0.1480        | -                         | -              |
+| 0.7249 | 7750 | 0.1937        | -                         | -              |
+| 0.7272 | 7775 | 0.1716        | -                         | -              |
+| 0.7296 | 7800 | 0.2077        | 0.2262                    | 0.0439         |
+| 0.7319 | 7825 | 0.2225        | -                         | -              |
+| 0.7343 | 7850 | 0.2039        | -                         | -              |
+| 0.7366 | 7875 | 0.2056        | -                         | -              |
+| 0.7389 | 7900 | 0.1970        | -                         | -              |
+| 0.7413 | 7925 | 0.2406        | -                         | -              |
+| 0.7436 | 7950 | 0.1784        | -                         | -              |
+| 0.7460 | 7975 | 0.1614        | -                         | -              |
+| 0.7483 | 8000 | 0.2377        | 0.2221                    | 0.0449         |
+| 0.7506 | 8025 | 0.1978        | -                         | -              |
+| 0.7530 | 8050 | 0.1876        | -                         | -              |
+| 0.7553 | 8075 | 0.1352        | -                         | -              |
+| 0.7576 | 8100 | 0.2114        | -                         | -              |
+| 0.7600 | 8125 | 0.1578        | -                         | -              |
+| 0.7623 | 8150 | 0.2274        | -                         | -              |
+| 0.7647 | 8175 | 0.2199        | -                         | -              |
+| 0.7670 | 8200 | 0.2234        | 0.2272                    | 0.0435         |
+| 0.7693 | 8225 | 0.1724        | -                         | -              |
+| 0.7717 | 8250 | 0.1656        | -                         | -              |
+| 0.7740 | 8275 | 0.1687        | -                         | -              |
+| 0.7764 | 8300 | 0.2358        | -                         | -              |
+| 0.7787 | 8325 | 0.2079        | -                         | -              |
+| 0.7810 | 8350 | 0.2063        | -                         | -              |
+| 0.7834 | 8375 | 0.2435        | -                         | -              |
+| 0.7857 | 8400 | 0.1544        | 0.2243                    | 0.0424         |
+| 0.7880 | 8425 | 0.1497        | -                         | -              |
+| 0.7904 | 8450 | 0.1938        | -                         | -              |
+| 0.7927 | 8475 | 0.1496        | -                         | -              |
+| 0.7951 | 8500 | 0.1530        | -                         | -              |
+| 0.7974 | 8525 | 0.1984        | -                         | -              |
+| 0.7997 | 8550 | 0.1646        | -                         | -              |
+| 0.8021 | 8575 | 0.1813        | -                         | -              |
+| 0.8044 | 8600 | 0.1722        | 0.2245                    | 0.0423         |
+| 0.8068 | 8625 | 0.2557        | -                         | -              |
+| 0.8091 | 8650 | 0.2175        | -                         | -              |
+| 0.8114 | 8675 | 0.1845        | -                         | -              |
+| 0.8138 | 8700 | 0.1734        | -                         | -              |
+| 0.8161 | 8725 | 0.1660        | -                         | -              |
+| 0.8184 | 8750 | 0.1915        | -                         | -              |
+| 0.8208 | 8775 | 0.1553        | -                         | -              |
+| 0.8231 | 8800 | 0.1875        | 0.2209                    | 0.0414         |
+| 0.8255 | 8825 | 0.1831        | -                         | -              |
+| 0.8278 | 8850 | 0.2149        | -                         | -              |
+| 0.8301 | 8875 | 0.1948        | -                         | -              |
+| 0.8325 | 8900 | 0.1599        | -                         | -              |
+| 0.8348 | 8925 | 0.1642        | -                         | -              |
+| 0.8372 | 8950 | 0.1799        | -                         | -              |
+| 0.8395 | 8975 | 0.2243        | -                         | -              |
+| 0.8418 | 9000 | 0.1701        | 0.2159                    | 0.0420         |
+| 0.8442 | 9025 | 0.2055        | -                         | -              |
+| 0.8465 | 9050 | 0.1869        | -                         | -              |
+| 0.8488 | 9075 | 0.1331        | -                         | -              |
+| 0.8512 | 9100 | 0.2021        | -                         | -              |
+| 0.8535 | 9125 | 0.1842        | -                         | -              |
+| 0.8559 | 9150 | 0.1386        | -                         | -              |
+| 0.8582 | 9175 | 0.1567        | -                         | -              |
+| 0.8605 | 9200 | 0.1775        | 0.2159                    | 0.0407         |
+| 0.8629 | 9225 | 0.1798        | -                         | -              |
+| 0.8652 | 9250 | 0.1724        | -                         | -              |
+| 0.8676 | 9275 | 0.2179        | -                         | -              |
+| 0.8699 | 9300 | 0.1428        | -                         | -              |
+| 0.8722 | 9325 | 0.1819        | -                         | -              |
+| 0.8746 | 9350 | 0.1991        | -                         | -              |
+| 0.8769 | 9375 | 0.1618        | -                         | -              |
+| 0.8792 | 9400 | 0.1833        | 0.2173                    | 0.0403         |
+| 0.8816 | 9425 | 0.1909        | -                         | -              |
+| 0.8839 | 9450 | 0.1964        | -                         | -              |
+| 0.8863 | 9475 | 0.1741        | -                         | -              |
+| 0.8886 | 9500 | 0.2163        | -                         | -              |
+| 0.8909 | 9525 | 0.1815        | -                         | -              |
+| 0.8933 | 9550 | 0.1854        | -                         | -              |
+| 0.8956 | 9575 | 0.1813        | -                         | -              |
+| 0.8980 | 9600 | 0.2050        | 0.2150                    | 0.0400         |
+| 0.9003 | 9625 | 0.1508        | -                         | -              |
+| 0.9026 | 9650 | 0.2091        | -                         | -              |
+| 0.9050 | 9675 | 0.2477        | -                         | -              |
+| 0.9073 | 9700 | 0.1604        | -                         | -              |
+| 0.9096 | 9725 | 0.2051        | -                         | -              |
+| 0.9120 | 9750 | 0.1931        | -                         | -              |
+| 0.9143 | 9775 | 0.1727        | -                         | -              |
+| 0.9167 | 9800 | 0.1735        | 0.2145                    | 0.0403         |
+
+</details>
+
+### Training Time
+- **Training**: 10.7 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-9800/adapter_config.json

@@ -0,0 +1,48 @@
+{
+  "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
+}

checkpoint-9800/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5c0f8253e1377a8d7a766e1f705dfd27318c5b074bf6cff2494e973441e7f3c5
+size 40419816

checkpoint-9800/chat_template.jinja

@@ -0,0 +1,85 @@
+{%- 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-9800/config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__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-9800/modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "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"
+  }
+]

checkpoint-9800/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:16eb303ddbe0cc85dc5c92a8a38ec64507dfc95765b587dff7fc9385aa5473bd
+size 81075515

checkpoint-9800/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76e88e8510d6e267a7fa4d2588cd76b4135e9224b46d02836927736fb1efcb22
+size 14581

checkpoint-9800/scaler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:118598cd33e57f21a3c5c0efc7f15bffeb5153448a22740b8a7a43d9540d2c58
+size 1383

checkpoint-9800/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4deaa896460cc3e46f48b2c5ffe0b3f3c53df901729faf32016e2b555e30bfa4
+size 1465

checkpoint-9800/sentence_bert_config.json

@@ -0,0 +1,10 @@
+{
+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

checkpoint-9800/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67b23115c7cace3b6700c333da5038e66e95a9c2c71531b6953563a8c8bb1dee
+size 11423967

checkpoint-9800/tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "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
+}

checkpoint-9800/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f64c13625404e7e8b2e3825b7c2b388aa17cbed32c8e2be6731de0c81a3f24a0
+size 5457