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1_SpladePooling/config.json

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+{
+    "pooling_strategy": "max",
+    "activation_function": "relu",
+    "embedding_dimension": 119547
+}

README.md

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+---
+language: ar
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sparse-encoder
+- splade
+- arabic
+- retrieval
+datasets:
+- oddadmix/arabic-triplets-large
+base_model: distilbert-base-multilingual-cased
+metrics:
+- ndcg@10
+- mrr@10
+---
+
+# Arabic SPLADE — Phase 3
+
+Efficient symmetric SPLADE using DistilBERT multilingual for faster inference.
+
+## Architecture
+
+Symmetric shared (MLMTransformer+SpladePooling, sequential)
+
+**Base model:** distilbert-base-multilingual-cased
+
+## Training
+
+- **Dataset:** `oddadmix/arabic-triplets-large` (104K triplets, 92K unique passages)
+- **Loss:** `SpladeLoss(SparseMultipleNegativesRankingLoss, q_reg=5e-5, d_reg=3e-5)`
+- **Batch:** 16 per GPU, grad accum 4
+- **Learning rate:** 2e-5
+- **Epochs:** 1
+- **AMP:** fp16
+- **Sampler:** NO_DUPLICATES
+
+## Evaluation on Arabic NanoBEIR (13 datasets)
+
+| Metric | Score |
+|--------|-------|
+| NDCG@10 | 0.2528 |
+| MRR@10 | 0.3052 |
+
+For reference: BM25 scores 0.3824 NDCG@10, 0.4483 MRR@10 on the same benchmark.
+
+## Training Details
+
+DistilBERT multilingual (6-layer, 119K vocab), ~2x faster than AraBERT
+
+### Hardware
+- 2× NVIDIA TITAN RTX (23.5 GB each)
+- DDP via `torchrun`
+
+## Usage
+
+```python
+from sentence_transformers.sparse_encoder import SparseEncoder
+
+model = SparseEncoder("Abdelkareem/arabic-splade-efficient")
+embeddings = model.encode([
+    "ما هي عاصمة مصر؟",
+    "القاهرة هي عاصمة مصر وأكبر مدنها.",
+])
+print(embeddings.shape)
+# Decode top tokens
+decoded = model.decode(embeddings, top_k=10)
+for d in decoded:
+    print(d)
+```

checkpoint-1000/1_SpladePooling/config.json

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+{
+    "pooling_strategy": "max",
+    "activation_function": "relu",
+    "embedding_dimension": 119547
+}

checkpoint-1000/README.md

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+---
+tags:
+- sentence-transformers
+- sparse-encoder
+- sparse
+- splade
+- generated_from_trainer
+- dataset_size:104550
+- loss:SpladeLoss
+- loss:SparseMultipleNegativesRankingLoss
+- loss:FlopsLoss
+base_model: distilbert/distilbert-base-multilingual-cased
+widget:
+- text: يُعتبر التأثير الأوروبي على الثقافة اليابانية في القرن التاسع عشر أمرًا هامًا
+    في فهم تاريخ البلاد.
+- text: "\n\n يعد النقد الأدبي أداة أساسية في فهم التاريخ الثقافي، حيث يعكس التغيرات\
+    \ الاجتماعية والثقافية التي حدثت عبر العصور."
+- text: لا أعتقد أن هناك أي تأثير لصالح المصممة الداخلية الإيطالية إيلينا فرونتزي
+    على هذا النوع من التصاميم.
+- text: كيف يؤثر النقد الأدبي على التفاعل الاجتماعي؟
+- text: "\n\n يعد النقد الأدبي أداة أساسية في فهم التاريخ الثقافي، حيث يساعد على تحليل\
+    \ وتفسير الأعمال الفنية والثقافية من خلال منظور تاريخي."
+datasets:
+- oddadmix/arabic-triplets-large
+pipeline_tag: feature-extraction
+library_name: sentence-transformers
+---
+
+# SPLADE Sparse Encoder
+
+This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the [arabic-triplets-large](https://huggingface.co/datasets/oddadmix/arabic-triplets-large) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 119547-dimensional sparse vector space   and can be used for semantic search and sparse retrieval.
+## Model Details
+
+### Model Description
+- **Model Type:** SPLADE Sparse Encoder
+- **Base model:** [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) <!-- at revision 45c032ab32cc946ad88a166f7cb282f58c753c2e -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 119547 dimensions
+- **Similarity Function:** Dot Product
+- **Supported Modality:** Text
+- **Training Dataset:**
+    - [arabic-triplets-large](https://huggingface.co/datasets/oddadmix/arabic-triplets-large)
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
+
+### Full Model Architecture
+
+```
+SparseEncoder(
+  (0): Transformer({'transformer_task': 'fill-mask', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'token_embeddings', 'architecture': 'DistilBertForMaskedLM'})
+  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'embedding_dimension': 119547})
+)
+```
+
+## 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 SparseEncoder
+
+# Download from the 🤗 Hub
+model = SparseEncoder("sparse_encoder_model_id")
+# Run inference
+sentences = [
+    'ما هي أهمية النقد الأدبي في فهم التاريخ الثقافي؟',
+    '\n\n يعد النقد الأدبي أداة أساسية في فهم التاريخ الثقافي، حيث يساعد على تحليل وتفسير الأعمال الفنية والثقافية من خلال منظور تاريخي.',
+    'تُعتبر اللغة العربية لغة قديمة ومتنوعة، وتمثل جزءًا هامًا من تراث البشرية.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 119547]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[30.0560, 33.2675,  8.7584],
+#         [33.2675, 72.6030, 13.4705],
+#         [ 8.7584, 13.4704, 75.8345]])
+```
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### arabic-triplets-large
+
+* Dataset: [arabic-triplets-large](https://huggingface.co/datasets/oddadmix/arabic-triplets-large) at [fa99ede](https://huggingface.co/datasets/oddadmix/arabic-triplets-large/tree/fa99ede10602ff5cffb7591ff1f25289414c4b13)
+* Size: 104,550 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 100 samples:
+  |          | anchor                                                                            | positive                                                                            | negative                                                                            |
+  |:---------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type     | string                                                                            | string                                                                              | string                                                                              |
+  | modality | text                                                                              | text                                                                                | text                                                                                |
+  | details  | <ul><li>min: 12 tokens</li><li>mean: 18.6 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 57.26 tokens</li><li>max: 142 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 47.38 tokens</li><li>max: 120 tokens</li></ul> |
+* Samples:
+  | anchor                                                          | positive                                                                                                                                                    | negative                                                                                                                                                                       |
+  |:----------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>ما هي أهمية التلال والمناطق الجبلية في البيئة؟</code>     | <code><br><br> تعتبر التلال والمناطق الجبلية من أهم عناصر البيئة التي تؤثر بشكل كبير على توازن النظام الإيكولوجي.</code>                                    | <code>يعتبر النشاط السياسي في البلدان الصغيرة من الأمور التي تتطلب إدارة شاملة ومتكاملة.</code>                                                                                |
+  | <code>كيف تؤثر التلال على الرياح والهطول المطر؟</code>          | <code><br><br> يؤثر التلال على الرياح والهطول المطر من خلال تأثيرهم على توزيع الضغط الجوي، مما يؤدي إلى تغييرات في اتجاه وسرعة الرياح وتواتر الهطول.</code> | <code>إنّ الأنشطة الزراعية في المناطق الجبلية تعتبر من أهمّ العوامل التي تساهم في تحسين جودة الحياة في هذه المناطق، ولكنها لا تؤثر بشكل مباشر على الرياح والهطول المطر.</code> |
+  | <code>ما هي أنواع التلال المختلفة؟ (جبال، هضاب، منحدرات)</code> | <code><br><br> هناك ثلاثة أنواع رئيسية للتلاءم هي الجبال، الهضاب، والمنحدرات.</code>                                                                        | <code>الإدارة البيئية تعتبر من الأنشطة التي لها تأثير كبير على البيئة.</code>                                                                                                  |
+* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
+  ```json
+  {
+      "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False, directions=('query_to_doc',), partition_mode='joint', hardness_mode=None, hardness_strength=0.0)",
+      "document_regularizer_weight": 3e-05,
+      "query_regularizer_weight": 5e-05
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `num_train_epochs`: 1
+- `learning_rate`: 2e-05
+- `gradient_accumulation_steps`: 4
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `per_device_train_batch_size`: 16
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `learning_rate`: 2e-05
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_steps`: 0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `optim_target_modules`: None
+- `gradient_accumulation_steps`: 4
+- `average_tokens_across_devices`: True
+- `max_grad_norm`: 1.0
+- `label_smoothing_factor`: 0.0
+- `bf16`: False
+- `fp16`: False
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `use_cache`: False
+- `neftune_noise_alpha`: None
+- `torch_empty_cache_steps`: None
+- `auto_find_batch_size`: False
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `include_num_input_tokens_seen`: no
+- `log_level`: passive
+- `log_level_replica`: warning
+- `disable_tqdm`: False
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `per_device_eval_batch_size`: 8
+- `prediction_loss_only`: True
+- `eval_on_start`: False
+- `eval_do_concat_batches`: True
+- `eval_use_gather_object`: False
+- `eval_accumulation_steps`: None
+- `include_for_metrics`: []
+- `batch_eval_metrics`: False
+- `save_only_model`: False
+- `save_on_each_node`: False
+- `enable_jit_checkpoint`: False
+- `push_to_hub`: False
+- `hub_private_repo`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_always_push`: False
+- `hub_revision`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `restore_callback_states_from_checkpoint`: False
+- `full_determinism`: False
+- `seed`: 42
+- `data_seed`: None
+- `use_cpu`: 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
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `dataloader_prefetch_factor`: None
+- `remove_unused_columns`: True
+- `label_names`: None
+- `train_sampling_strategy`: random
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `ddp_backend`: None
+- `ddp_timeout`: 1800
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `deepspeed`: None
+- `debug`: []
+- `skip_memory_metrics`: True
+- `do_predict`: False
+- `resume_from_checkpoint`: None
+- `warmup_ratio`: None
+- `local_rank`: -1
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.3060 | 500  | 7.9967        |
+| 0.6121 | 1000 | 0.0165        |
+
+
+### Training Time
+- **Training**: 16.7 minutes
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.5.1
+- Transformers: 5.5.0
+- PyTorch: 2.12.0+cu130
+- Accelerate: 1.14.0
+- Datasets: 4.3.0
+- Tokenizers: 0.22.2
+
+## Additional Resources
+
+- [Training and Finetuning Sparse Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sparse-encoder): the end-to-end guide for training or finetuning SPLADE and other sparse encoder models.
+
+## 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",
+}
+```
+
+#### SpladeLoss
+```bibtex
+@misc{formal2022distillationhardnegativesampling,
+      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
+      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
+      year={2022},
+      eprint={2205.04733},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2205.04733},
+}
+```
+
+#### SparseMultipleNegativesRankingLoss
+```bibtex
+@misc{oord2019representationlearningcontrastivepredictive,
+      title={Representation Learning with Contrastive Predictive Coding},
+      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
+      year={2019},
+      eprint={1807.03748},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1807.03748},
+}
+```
+
+#### FlopsLoss
+```bibtex
+@article{paria2020minimizing,
+    title={Minimizing flops to learn efficient sparse representations},
+    author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
+    journal={arXiv preprint arXiv:2004.05665},
+    year={2020}
+}
+```
+
+<!--
+## 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-1000/config.json

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+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForMaskedLM"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": null,
+  "dim": 768,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": false,
+  "vocab_size": 119547
+}

checkpoint-1000/config_sentence_transformers.json

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+{
+  "__version__": {
+    "pytorch": "2.12.0+cu130",
+    "sentence_transformers": "5.5.1",
+    "transformers": "5.5.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "SparseEncoder",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "dot"
+}

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checkpoint-1000/modules.json

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+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.sparse_encoder.modules.mlm_transformer.MLMTransformer"
+  },
+  {
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+    "name": "1",
+    "path": "1_SpladePooling",
+    "type": "sentence_transformers.sparse_encoder.modules.splade_pooling.SpladePooling"
+  }
+]

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checkpoint-1000/sentence_bert_config.json

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+{
+    "transformer_task": "fill-mask",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "logits"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

checkpoint-1000/tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

checkpoint-1000/trainer_state.json

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+      "args": {
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+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 0.0,
+  "train_batch_size": 16,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-1500/1_SpladePooling/config.json

@@ -0,0 +1,5 @@
+{
+    "pooling_strategy": "max",
+    "activation_function": "relu",
+    "embedding_dimension": 119547
+}

checkpoint-1500/README.md

@@ -0,0 +1,364 @@
+---
+tags:
+- sentence-transformers
+- sparse-encoder
+- sparse
+- splade
+- generated_from_trainer
+- dataset_size:104550
+- loss:SpladeLoss
+- loss:SparseMultipleNegativesRankingLoss
+- loss:FlopsLoss
+base_model: distilbert/distilbert-base-multilingual-cased
+widget:
+- text: يُعتبر التأثير الأوروبي على الثقافة اليابانية في القرن التاسع عشر أمرًا هامًا
+    في فهم تاريخ البلاد.
+- text: "\n\n يعد النقد الأدبي أداة أساسية في فهم التاريخ الثقافي، حيث يعكس التغيرات\
+    \ الاجتماعية والثقافية التي حدثت عبر العصور."
+- text: لا أعتقد أن هناك أي تأثير لصالح المصممة الداخلية الإيطالية إيلينا فرونتزي
+    على هذا النوع من التصاميم.
+- text: كيف يؤثر النقد الأدبي على التفاعل الاجتماعي؟
+- text: "\n\n يعد النقد الأدبي أداة أساسية في فهم التاريخ الثقافي، حيث يساعد على تحليل\
+    \ وتفسير الأعمال الفنية والثقافية من خلال منظور تاريخي."
+datasets:
+- oddadmix/arabic-triplets-large
+pipeline_tag: feature-extraction
+library_name: sentence-transformers
+---
+
+# SPLADE Sparse Encoder
+
+This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the [arabic-triplets-large](https://huggingface.co/datasets/oddadmix/arabic-triplets-large) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 119547-dimensional sparse vector space   and can be used for semantic search and sparse retrieval.
+## Model Details
+
+### Model Description
+- **Model Type:** SPLADE Sparse Encoder
+- **Base model:** [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) <!-- at revision 45c032ab32cc946ad88a166f7cb282f58c753c2e -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 119547 dimensions
+- **Similarity Function:** Dot Product
+- **Supported Modality:** Text
+- **Training Dataset:**
+    - [arabic-triplets-large](https://huggingface.co/datasets/oddadmix/arabic-triplets-large)
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
+
+### Full Model Architecture
+
+```
+SparseEncoder(
+  (0): Transformer({'transformer_task': 'fill-mask', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'token_embeddings', 'architecture': 'DistilBertForMaskedLM'})
+  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'embedding_dimension': 119547})
+)
+```
+
+## 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 SparseEncoder
+
+# Download from the 🤗 Hub
+model = SparseEncoder("sparse_encoder_model_id")
+# Run inference
+sentences = [
+    'ما هي أهمية النقد الأدبي في فهم التاريخ الثقافي؟',
+    '\n\n يعد النقد الأدبي أداة أساسية في فهم التاريخ الثقافي، حيث يساعد على تحليل وتفسير الأعمال الفنية والثقافية من خلال منظور تاريخي.',
+    'تُعتبر اللغة العربية لغة قديمة ومتنوعة، وتمثل جزءًا هامًا من تراث البشرية.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 119547]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[38.0133, 42.8860,  6.6607],
+#         [42.8860, 93.9162, 12.8581],
+#         [ 6.6607, 12.8581, 96.7075]])
+```
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### arabic-triplets-large
+
+* Dataset: [arabic-triplets-large](https://huggingface.co/datasets/oddadmix/arabic-triplets-large) at [fa99ede](https://huggingface.co/datasets/oddadmix/arabic-triplets-large/tree/fa99ede10602ff5cffb7591ff1f25289414c4b13)
+* Size: 104,550 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 100 samples:
+  |          | anchor                                                                            | positive                                                                            | negative                                                                            |
+  |:---------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type     | string                                                                            | string                                                                              | string                                                                              |
+  | modality | text                                                                              | text                                                                                | text                                                                                |
+  | details  | <ul><li>min: 12 tokens</li><li>mean: 18.6 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 57.26 tokens</li><li>max: 142 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 47.38 tokens</li><li>max: 120 tokens</li></ul> |
+* Samples:
+  | anchor                                                          | positive                                                                                                                                                    | negative                                                                                                                                                                       |
+  |:----------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>ما هي أهمية التلال والمناطق الجبلية في البيئة؟</code>     | <code><br><br> تعتبر التلال والمناطق الجبلية من أهم عناصر البيئة التي تؤثر بشكل كبير على توازن النظام الإيكولوجي.</code>                                    | <code>يعتبر النشاط السياسي في البلدان الصغيرة من الأمور التي تتطلب إدارة شاملة ومتكاملة.</code>                                                                                |
+  | <code>كيف تؤثر التلال على الرياح والهطول المطر؟</code>          | <code><br><br> يؤثر التلال على الرياح والهطول المطر من خلال تأثيرهم على توزيع الضغط الجوي، مما يؤدي إلى تغييرات في اتجاه وسرعة الرياح وتواتر الهطول.</code> | <code>إنّ الأنشطة الزراعية في المناطق الجبلية تعتبر من أهمّ العوامل التي تساهم في تحسين جودة الحياة في هذه المناطق، ولكنها لا تؤثر بشكل مباشر على الرياح والهطول المطر.</code> |
+  | <code>ما هي أنواع التلال المختلفة؟ (جبال، هضاب، منحدرات)</code> | <code><br><br> هناك ثلاثة أنواع رئيسية للتلاءم هي الجبال، الهضاب، والمنحدرات.</code>                                                                        | <code>الإدارة البيئية تعتبر من الأنشطة التي لها تأثير كبير على البيئة.</code>                                                                                                  |
+* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
+  ```json
+  {
+      "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False, directions=('query_to_doc',), partition_mode='joint', hardness_mode=None, hardness_strength=0.0)",
+      "document_regularizer_weight": 3e-05,
+      "query_regularizer_weight": 5e-05
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `num_train_epochs`: 1
+- `learning_rate`: 2e-05
+- `gradient_accumulation_steps`: 4
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `per_device_train_batch_size`: 16
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `learning_rate`: 2e-05
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_steps`: 0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `optim_target_modules`: None
+- `gradient_accumulation_steps`: 4
+- `average_tokens_across_devices`: True
+- `max_grad_norm`: 1.0
+- `label_smoothing_factor`: 0.0
+- `bf16`: False
+- `fp16`: False
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `use_cache`: False
+- `neftune_noise_alpha`: None
+- `torch_empty_cache_steps`: None
+- `auto_find_batch_size`: False
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `include_num_input_tokens_seen`: no
+- `log_level`: passive
+- `log_level_replica`: warning
+- `disable_tqdm`: False
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `per_device_eval_batch_size`: 8
+- `prediction_loss_only`: True
+- `eval_on_start`: False
+- `eval_do_concat_batches`: True
+- `eval_use_gather_object`: False
+- `eval_accumulation_steps`: None
+- `include_for_metrics`: []
+- `batch_eval_metrics`: False
+- `save_only_model`: False
+- `save_on_each_node`: False
+- `enable_jit_checkpoint`: False
+- `push_to_hub`: False
+- `hub_private_repo`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_always_push`: False
+- `hub_revision`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `restore_callback_states_from_checkpoint`: False
+- `full_determinism`: False
+- `seed`: 42
+- `data_seed`: None
+- `use_cpu`: 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
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `dataloader_prefetch_factor`: None
+- `remove_unused_columns`: True
+- `label_names`: None
+- `train_sampling_strategy`: random
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `ddp_backend`: None
+- `ddp_timeout`: 1800
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `deepspeed`: None
+- `debug`: []
+- `skip_memory_metrics`: True
+- `do_predict`: False
+- `resume_from_checkpoint`: None
+- `warmup_ratio`: None
+- `local_rank`: -1
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.3060 | 500  | 7.9967        |
+| 0.6121 | 1000 | 0.0165        |
+| 0.9181 | 1500 | 0.0108        |
+
+
+### Training Time
+- **Training**: 25.0 minutes
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.5.1
+- Transformers: 5.5.0
+- PyTorch: 2.12.0+cu130
+- Accelerate: 1.14.0
+- Datasets: 4.3.0
+- Tokenizers: 0.22.2
+
+## Additional Resources
+
+- [Training and Finetuning Sparse Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sparse-encoder): the end-to-end guide for training or finetuning SPLADE and other sparse encoder models.
+
+## 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",
+}
+```
+
+#### SpladeLoss
+```bibtex
+@misc{formal2022distillationhardnegativesampling,
+      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
+      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
+      year={2022},
+      eprint={2205.04733},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2205.04733},
+}
+```
+
+#### SparseMultipleNegativesRankingLoss
+```bibtex
+@misc{oord2019representationlearningcontrastivepredictive,
+      title={Representation Learning with Contrastive Predictive Coding},
+      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
+      year={2019},
+      eprint={1807.03748},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1807.03748},
+}
+```
+
+#### FlopsLoss
+```bibtex
+@article{paria2020minimizing,
+    title={Minimizing flops to learn efficient sparse representations},
+    author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
+    journal={arXiv preprint arXiv:2004.05665},
+    year={2020}
+}
+```
+
+<!--
+## 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-1500/config.json

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+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForMaskedLM"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": null,
+  "dim": 768,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": false,
+  "vocab_size": 119547
+}

checkpoint-1500/config_sentence_transformers.json

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+{
+  "__version__": {
+    "pytorch": "2.12.0+cu130",
+    "sentence_transformers": "5.5.1",
+    "transformers": "5.5.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "SparseEncoder",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "dot"
+}

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checkpoint-1500/modules.json

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+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.sparse_encoder.modules.mlm_transformer.MLMTransformer"
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+    "name": "1",
+    "path": "1_SpladePooling",
+    "type": "sentence_transformers.sparse_encoder.modules.splade_pooling.SpladePooling"
+  }
+]

checkpoint-1500/rng_state.pth

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checkpoint-1500/sentence_bert_config.json

@@ -0,0 +1,10 @@
+{
+    "transformer_task": "fill-mask",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "logits"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

checkpoint-1500/tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

checkpoint-1500/trainer_state.json

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+  "max_steps": 1634,
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+  "num_train_epochs": 1,
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+  "stateful_callbacks": {
+    "TrainerControl": {
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+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
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+}

checkpoint-1634/1_SpladePooling/config.json

@@ -0,0 +1,5 @@
+{
+    "pooling_strategy": "max",
+    "activation_function": "relu",
+    "embedding_dimension": 119547
+}

checkpoint-1634/README.md

@@ -0,0 +1,364 @@
+---
+tags:
+- sentence-transformers
+- sparse-encoder
+- sparse
+- splade
+- generated_from_trainer
+- dataset_size:104550
+- loss:SpladeLoss
+- loss:SparseMultipleNegativesRankingLoss
+- loss:FlopsLoss
+base_model: distilbert/distilbert-base-multilingual-cased
+widget:
+- text: يُعتبر التأثير الأوروبي على الثقافة اليابانية في القرن التاسع عشر أمرًا هامًا
+    في فهم تاريخ البلاد.
+- text: "\n\n يعد النقد الأدبي أداة أساسية في فهم التاريخ الثقافي، حيث يعكس التغيرات\
+    \ الاجتماعية والثقافية التي حدثت عبر العصور."
+- text: لا أعتقد أن هناك أي تأثير لصالح المصممة الداخلية الإيطالية إيلينا فرونتزي
+    على هذا النوع من التصاميم.
+- text: كيف يؤثر النقد الأدبي على التفاعل الاجتماعي؟
+- text: "\n\n يعد النقد الأدبي أداة أساسية في فهم التاريخ الثقافي، حيث يساعد على تحليل\
+    \ وتفسير الأعمال الفنية والثقافية من خلال منظور تاريخي."
+datasets:
+- oddadmix/arabic-triplets-large
+pipeline_tag: feature-extraction
+library_name: sentence-transformers
+---
+
+# SPLADE Sparse Encoder
+
+This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the [arabic-triplets-large](https://huggingface.co/datasets/oddadmix/arabic-triplets-large) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 119547-dimensional sparse vector space   and can be used for semantic search and sparse retrieval.
+## Model Details
+
+### Model Description
+- **Model Type:** SPLADE Sparse Encoder
+- **Base model:** [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) <!-- at revision 45c032ab32cc946ad88a166f7cb282f58c753c2e -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 119547 dimensions
+- **Similarity Function:** Dot Product
+- **Supported Modality:** Text
+- **Training Dataset:**
+    - [arabic-triplets-large](https://huggingface.co/datasets/oddadmix/arabic-triplets-large)
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
+
+### Full Model Architecture
+
+```
+SparseEncoder(
+  (0): Transformer({'transformer_task': 'fill-mask', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'token_embeddings', 'architecture': 'DistilBertForMaskedLM'})
+  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'embedding_dimension': 119547})
+)
+```
+
+## 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 SparseEncoder
+
+# Download from the 🤗 Hub
+model = SparseEncoder("sparse_encoder_model_id")
+# Run inference
+sentences = [
+    'ما هي أهمية النقد الأدبي في فهم التاريخ الثقافي؟',
+    '\n\n يعد النقد الأدبي أداة أساسية في فهم التاريخ الثقافي، حيث يساعد على تحليل وتفسير الأعمال الفنية والثقافية من خلال منظور تاريخي.',
+    'تُعتبر اللغة العربية لغة قديمة ومتنوعة، وتمثل جزءًا هامًا من تراث البشرية.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 119547]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[34.2188, 38.5133,  6.4171],
+#         [38.5133, 83.0456, 11.4468],
+#         [ 6.4171, 11.4468, 90.0776]])
+```
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### arabic-triplets-large
+
+* Dataset: [arabic-triplets-large](https://huggingface.co/datasets/oddadmix/arabic-triplets-large) at [fa99ede](https://huggingface.co/datasets/oddadmix/arabic-triplets-large/tree/fa99ede10602ff5cffb7591ff1f25289414c4b13)
+* Size: 104,550 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 100 samples:
+  |          | anchor                                                                            | positive                                                                            | negative                                                                            |
+  |:---------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type     | string                                                                            | string                                                                              | string                                                                              |
+  | modality | text                                                                              | text                                                                                | text                                                                                |
+  | details  | <ul><li>min: 12 tokens</li><li>mean: 18.6 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 57.26 tokens</li><li>max: 142 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 47.38 tokens</li><li>max: 120 tokens</li></ul> |
+* Samples:
+  | anchor                                                          | positive                                                                                                                                                    | negative                                                                                                                                                                       |
+  |:----------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>ما هي أهمية التلال والمناطق الجبلية في البيئة؟</code>     | <code><br><br> تعتبر التلال والمناطق الجبلية من أهم عناصر البيئة التي تؤثر بشكل كبير على توازن النظام الإيكولوجي.</code>                                    | <code>يعتبر النشاط السياسي في البلدان الصغيرة من الأمور التي تتطلب إدارة شاملة ومتكاملة.</code>                                                                                |
+  | <code>كيف تؤثر التلال على الرياح والهطول المطر؟</code>          | <code><br><br> يؤثر التلال على الرياح والهطول المطر من خلال تأثيرهم على توزيع الضغط الجوي، مما يؤدي إلى تغييرات في اتجاه وسرعة الرياح وتواتر الهطول.</code> | <code>إنّ الأنشطة الزراعية في المناطق الجبلية تعتبر من أهمّ العوامل التي تساهم في تحسين جودة الحياة في هذه المناطق، ولكنها لا تؤثر بشكل مباشر على الرياح والهطول المطر.</code> |
+  | <code>ما هي أنواع التلال المختلفة؟ (جبال، هضاب، منحدرات)</code> | <code><br><br> هناك ثلاثة أنواع رئيسية للتلاءم هي الجبال، الهضاب، والمنحدرات.</code>                                                                        | <code>الإدارة البيئية تعتبر من الأنشطة التي لها تأثير كبير على البيئة.</code>                                                                                                  |
+* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
+  ```json
+  {
+      "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False, directions=('query_to_doc',), partition_mode='joint', hardness_mode=None, hardness_strength=0.0)",
+      "document_regularizer_weight": 3e-05,
+      "query_regularizer_weight": 5e-05
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `num_train_epochs`: 1
+- `learning_rate`: 2e-05
+- `gradient_accumulation_steps`: 4
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `per_device_train_batch_size`: 16
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `learning_rate`: 2e-05
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_steps`: 0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `optim_target_modules`: None
+- `gradient_accumulation_steps`: 4
+- `average_tokens_across_devices`: True
+- `max_grad_norm`: 1.0
+- `label_smoothing_factor`: 0.0
+- `bf16`: False
+- `fp16`: False
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `use_cache`: False
+- `neftune_noise_alpha`: None
+- `torch_empty_cache_steps`: None
+- `auto_find_batch_size`: False
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `include_num_input_tokens_seen`: no
+- `log_level`: passive
+- `log_level_replica`: warning
+- `disable_tqdm`: False
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `per_device_eval_batch_size`: 8
+- `prediction_loss_only`: True
+- `eval_on_start`: False
+- `eval_do_concat_batches`: True
+- `eval_use_gather_object`: False
+- `eval_accumulation_steps`: None
+- `include_for_metrics`: []
+- `batch_eval_metrics`: False
+- `save_only_model`: False
+- `save_on_each_node`: False
+- `enable_jit_checkpoint`: False
+- `push_to_hub`: False
+- `hub_private_repo`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_always_push`: False
+- `hub_revision`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `restore_callback_states_from_checkpoint`: False
+- `full_determinism`: False
+- `seed`: 42
+- `data_seed`: None
+- `use_cpu`: 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
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `dataloader_prefetch_factor`: None
+- `remove_unused_columns`: True
+- `label_names`: None
+- `train_sampling_strategy`: random
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `ddp_backend`: None
+- `ddp_timeout`: 1800
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `deepspeed`: None
+- `debug`: []
+- `skip_memory_metrics`: True
+- `do_predict`: False
+- `resume_from_checkpoint`: None
+- `warmup_ratio`: None
+- `local_rank`: -1
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.3060 | 500  | 7.9967        |
+| 0.6121 | 1000 | 0.0165        |
+| 0.9181 | 1500 | 0.0108        |
+
+
+### Training Time
+- **Training**: 27.2 minutes
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.5.1
+- Transformers: 5.5.0
+- PyTorch: 2.12.0+cu130
+- Accelerate: 1.14.0
+- Datasets: 4.3.0
+- Tokenizers: 0.22.2
+
+## Additional Resources
+
+- [Training and Finetuning Sparse Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sparse-encoder): the end-to-end guide for training or finetuning SPLADE and other sparse encoder models.
+
+## 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",
+}
+```
+
+#### SpladeLoss
+```bibtex
+@misc{formal2022distillationhardnegativesampling,
+      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
+      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
+      year={2022},
+      eprint={2205.04733},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2205.04733},
+}
+```
+
+#### SparseMultipleNegativesRankingLoss
+```bibtex
+@misc{oord2019representationlearningcontrastivepredictive,
+      title={Representation Learning with Contrastive Predictive Coding},
+      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
+      year={2019},
+      eprint={1807.03748},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1807.03748},
+}
+```
+
+#### FlopsLoss
+```bibtex
+@article{paria2020minimizing,
+    title={Minimizing flops to learn efficient sparse representations},
+    author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
+    journal={arXiv preprint arXiv:2004.05665},
+    year={2020}
+}
+```
+
+<!--
+## 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-1634/config.json

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+{
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+  "architectures": [
+    "DistilBertForMaskedLM"
+  ],
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+  "model_type": "distilbert",
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+  "n_layers": 6,
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+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": false,
+  "vocab_size": 119547
+}

checkpoint-1634/config_sentence_transformers.json

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+{
+  "__version__": {
+    "pytorch": "2.12.0+cu130",
+    "sentence_transformers": "5.5.1",
+    "transformers": "5.5.0"
+  },
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+  "model_type": "SparseEncoder",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "dot"
+}

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checkpoint-1634/modules.json

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checkpoint-1634/sentence_bert_config.json

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+{
+    "transformer_task": "fill-mask",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "logits"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

checkpoint-1634/tokenizer_config.json

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+  "backend": "tokenizers",
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+  "unk_token": "[UNK]"
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checkpoint-1634/trainer_state.json

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checkpoint-500/1_SpladePooling/config.json

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+{
+    "pooling_strategy": "max",
+    "activation_function": "relu",
+    "embedding_dimension": 119547
+}

checkpoint-500/README.md

@@ -0,0 +1,362 @@
+---
+tags:
+- sentence-transformers
+- sparse-encoder
+- sparse
+- splade
+- generated_from_trainer
+- dataset_size:104550
+- loss:SpladeLoss
+- loss:SparseMultipleNegativesRankingLoss
+- loss:FlopsLoss
+base_model: distilbert/distilbert-base-multilingual-cased
+widget:
+- text: يُعتبر التأثير الأوروبي على الثقافة اليابانية في القرن التاسع عشر أمرًا هامًا
+    في فهم تاريخ البلاد.
+- text: "\n\n يعد النقد الأدبي أداة أساسية في فهم التاريخ الثقافي، حيث يعكس التغيرات\
+    \ الاجتماعية والثقافية التي حدثت عبر العصور."
+- text: لا أعتقد أن هناك أي تأثير لصالح المصممة الداخلية الإيطالية إيلينا فرونتزي
+    على هذا النوع من التصاميم.
+- text: كيف يؤثر النقد الأدبي على التفاعل الاجتماعي؟
+- text: "\n\n يعد النقد الأدبي أداة أساسية في فهم التاريخ الثقافي، حيث يساعد على تحليل\
+    \ وتفسير الأعمال الفنية والثقافية من خلال منظور تاريخي."
+datasets:
+- oddadmix/arabic-triplets-large
+pipeline_tag: feature-extraction
+library_name: sentence-transformers
+---
+
+# SPLADE Sparse Encoder
+
+This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the [arabic-triplets-large](https://huggingface.co/datasets/oddadmix/arabic-triplets-large) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 119547-dimensional sparse vector space   and can be used for semantic search and sparse retrieval.
+## Model Details
+
+### Model Description
+- **Model Type:** SPLADE Sparse Encoder
+- **Base model:** [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) <!-- at revision 45c032ab32cc946ad88a166f7cb282f58c753c2e -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 119547 dimensions
+- **Similarity Function:** Dot Product
+- **Supported Modality:** Text
+- **Training Dataset:**
+    - [arabic-triplets-large](https://huggingface.co/datasets/oddadmix/arabic-triplets-large)
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
+
+### Full Model Architecture
+
+```
+SparseEncoder(
+  (0): Transformer({'transformer_task': 'fill-mask', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'token_embeddings', 'architecture': 'DistilBertForMaskedLM'})
+  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'embedding_dimension': 119547})
+)
+```
+
+## 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 SparseEncoder
+
+# Download from the 🤗 Hub
+model = SparseEncoder("sparse_encoder_model_id")
+# Run inference
+sentences = [
+    'ما هي أهمية النقد الأدبي في فهم التاريخ الثقافي؟',
+    '\n\n يعد النقد الأدبي أداة أساسية في فهم التاريخ الثقافي، حيث يساعد على تحليل وتفسير الأعمال الفنية والثقافية من خلال منظور تاريخي.',
+    'تُعتبر اللغة العربية لغة قديمة ومتنوعة، وتمثل جزءًا هامًا من تراث البشرية.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 119547]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[ 43.2637,  49.1410,  12.4760],
+#         [ 49.1410, 113.4840,  30.3306],
+#         [ 12.4760,  30.3306, 109.0792]])
+```
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### arabic-triplets-large
+
+* Dataset: [arabic-triplets-large](https://huggingface.co/datasets/oddadmix/arabic-triplets-large) at [fa99ede](https://huggingface.co/datasets/oddadmix/arabic-triplets-large/tree/fa99ede10602ff5cffb7591ff1f25289414c4b13)
+* Size: 104,550 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 100 samples:
+  |          | anchor                                                                            | positive                                                                            | negative                                                                            |
+  |:---------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type     | string                                                                            | string                                                                              | string                                                                              |
+  | modality | text                                                                              | text                                                                                | text                                                                                |
+  | details  | <ul><li>min: 12 tokens</li><li>mean: 18.6 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 57.26 tokens</li><li>max: 142 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 47.38 tokens</li><li>max: 120 tokens</li></ul> |
+* Samples:
+  | anchor                                                          | positive                                                                                                                                                    | negative                                                                                                                                                                       |
+  |:----------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>ما هي أهمية التلال والمناطق الجبلية في البيئة؟</code>     | <code><br><br> تعتبر التلال والمناطق الجبلية من أهم عناصر البيئة التي تؤثر بشكل كبير على توازن النظام الإيكولوجي.</code>                                    | <code>يعتبر النشاط السياسي في البلدان الصغيرة من الأمور التي تتطلب إدارة شاملة ومتكاملة.</code>                                                                                |
+  | <code>كيف تؤثر التلال على الرياح والهطول المطر؟</code>          | <code><br><br> يؤثر التلال على الرياح والهطول المطر من خلال تأثيرهم على توزيع الضغط الجوي، مما يؤدي إلى تغييرات في اتجاه وسرعة الرياح وتواتر الهطول.</code> | <code>إنّ الأنشطة الزراعية في المناطق الجبلية تعتبر من أهمّ العوامل التي تساهم في تحسين جودة الحياة في هذه المناطق، ولكنها لا تؤثر بشكل مباشر على الرياح والهطول المطر.</code> |
+  | <code>ما هي أنواع التلال المختلفة؟ (جبال، هضاب، منحدرات)</code> | <code><br><br> هناك ثلاثة أنواع رئيسية للتلاءم هي الجبال، الهضاب، والمنحدرات.</code>                                                                        | <code>الإدارة البيئية تعتبر من الأنشطة التي لها تأثير كبير على البيئة.</code>                                                                                                  |
+* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
+  ```json
+  {
+      "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False, directions=('query_to_doc',), partition_mode='joint', hardness_mode=None, hardness_strength=0.0)",
+      "document_regularizer_weight": 3e-05,
+      "query_regularizer_weight": 5e-05
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `num_train_epochs`: 1
+- `learning_rate`: 2e-05
+- `gradient_accumulation_steps`: 4
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `per_device_train_batch_size`: 16
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `learning_rate`: 2e-05
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_steps`: 0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `optim_target_modules`: None
+- `gradient_accumulation_steps`: 4
+- `average_tokens_across_devices`: True
+- `max_grad_norm`: 1.0
+- `label_smoothing_factor`: 0.0
+- `bf16`: False
+- `fp16`: False
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `use_cache`: False
+- `neftune_noise_alpha`: None
+- `torch_empty_cache_steps`: None
+- `auto_find_batch_size`: False
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `include_num_input_tokens_seen`: no
+- `log_level`: passive
+- `log_level_replica`: warning
+- `disable_tqdm`: False
+- `project`: huggingface
+- `trackio_space_id`: trackio
+- `per_device_eval_batch_size`: 8
+- `prediction_loss_only`: True
+- `eval_on_start`: False
+- `eval_do_concat_batches`: True
+- `eval_use_gather_object`: False
+- `eval_accumulation_steps`: None
+- `include_for_metrics`: []
+- `batch_eval_metrics`: False
+- `save_only_model`: False
+- `save_on_each_node`: False
+- `enable_jit_checkpoint`: False
+- `push_to_hub`: False
+- `hub_private_repo`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_always_push`: False
+- `hub_revision`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `restore_callback_states_from_checkpoint`: False
+- `full_determinism`: False
+- `seed`: 42
+- `data_seed`: None
+- `use_cpu`: 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
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `dataloader_prefetch_factor`: None
+- `remove_unused_columns`: True
+- `label_names`: None
+- `train_sampling_strategy`: random
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `ddp_backend`: None
+- `ddp_timeout`: 1800
+- `fsdp`: []
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `deepspeed`: None
+- `debug`: []
+- `skip_memory_metrics`: True
+- `do_predict`: False
+- `resume_from_checkpoint`: None
+- `warmup_ratio`: None
+- `local_rank`: -1
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.3060 | 500  | 7.9967        |
+
+
+### Training Time
+- **Training**: 8.3 minutes
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.5.1
+- Transformers: 5.5.0
+- PyTorch: 2.12.0+cu130
+- Accelerate: 1.14.0
+- Datasets: 4.3.0
+- Tokenizers: 0.22.2
+
+## Additional Resources
+
+- [Training and Finetuning Sparse Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sparse-encoder): the end-to-end guide for training or finetuning SPLADE and other sparse encoder models.
+
+## 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",
+}
+```
+
+#### SpladeLoss
+```bibtex
+@misc{formal2022distillationhardnegativesampling,
+      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
+      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
+      year={2022},
+      eprint={2205.04733},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2205.04733},
+}
+```
+
+#### SparseMultipleNegativesRankingLoss
+```bibtex
+@misc{oord2019representationlearningcontrastivepredictive,
+      title={Representation Learning with Contrastive Predictive Coding},
+      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
+      year={2019},
+      eprint={1807.03748},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1807.03748},
+}
+```
+
+#### FlopsLoss
+```bibtex
+@article{paria2020minimizing,
+    title={Minimizing flops to learn efficient sparse representations},
+    author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
+    journal={arXiv preprint arXiv:2004.05665},
+    year={2020}
+}
+```
+
+<!--
+## 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-500/config.json

@@ -0,0 +1,28 @@
+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForMaskedLM"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": null,
+  "dim": 768,
+  "dropout": 0.1,
+  "dtype": "float32",
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+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": false,
+  "vocab_size": 119547
+}

checkpoint-500/config_sentence_transformers.json

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+{
+  "__version__": {
+    "pytorch": "2.12.0+cu130",
+    "sentence_transformers": "5.5.1",
+    "transformers": "5.5.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "SparseEncoder",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": null
+}

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checkpoint-500/modules.json

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+  {
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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.sparse_encoder.modules.mlm_transformer.MLMTransformer"
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+    "idx": 1,
+    "name": "1",
+    "path": "1_SpladePooling",
+    "type": "sentence_transformers.sparse_encoder.modules.splade_pooling.SpladePooling"
+  }
+]

checkpoint-500/rng_state.pth

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checkpoint-500/sentence_bert_config.json

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+{
+    "transformer_task": "fill-mask",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "logits"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

checkpoint-500/tokenizer_config.json

@@ -0,0 +1,14 @@
+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

checkpoint-500/trainer_state.json

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+  "best_metric": null,
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config.json

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+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForMaskedLM"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": null,
+  "dim": 768,
+  "dropout": 0.1,
+  "dtype": "float32",
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+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": false,
+  "vocab_size": 119547
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "pytorch": "2.12.0+cu130",
+    "sentence_transformers": "5.5.1",
+    "transformers": "5.5.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "SparseEncoder",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "dot"
+}

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modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.sparse_encoder.modules.mlm_transformer.MLMTransformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_SpladePooling",
+    "type": "sentence_transformers.sparse_encoder.modules.splade_pooling.SpladePooling"
+  }
+]