---
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([[ 0.6773, -0.0849, -0.0875]])
```
<!--
### 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         |
| 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         |


### Training Time
- **Training**: 2.2 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}
}
```

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