Add new SparseEncoder model

README.md

@@ -0,0 +1,537 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sparse-encoder
+- sparse
+- asymmetric
+- inference-free
+- splade
+- generated_from_trainer
+- dataset_size:9000
+- loss:SpladeLoss
+- loss:SparseMultipleNegativesRankingLoss
+- loss:FlopsLoss
+- dataset_size:89000
+base_model: distilbert/distilbert-base-uncased
+widget:
+- text: Blank Neoprene Water Bottle Coolies (Variety Color 10 Pack)
+- text: Dream Spa 3-way 8-Setting Rainfall Shower Head and Handheld Shower Combo (Chrome).
+    Use Luxury 7-inch Rain Showerhead or 7-Function Hand Shower for Ultimate Spa Experience!
+- text: ¿Está disponible el nuevo iPhone 7 Plus?
+- text: Naipo Back Massager Massage Chair Vibrating Car Seat Cushion for Back, Neck,
+    and Thigh with 8 Motor Vibrations 4 Modes 3 Speed Heating at Home Office Car
+- text: Pizuna 400 Thread Count Cotton Fitted-Sheet Queen Size White 1pc, 100% Long
+    Staple Cotton Sateen Fitted Bed Sheet With All Around Elastic Deep Pocket Queen
+    Sheets Fit Up to 15Inch (White Fitted Sheet)
+pipeline_tag: feature-extraction
+library_name: sentence-transformers
+metrics:
+- dot_accuracy@1
+- dot_accuracy@3
+- dot_accuracy@5
+- dot_accuracy@10
+- dot_precision@1
+- dot_precision@3
+- dot_precision@5
+- dot_precision@10
+- dot_recall@1
+- dot_recall@3
+- dot_recall@5
+- dot_recall@10
+- dot_ndcg@10
+- dot_mrr@10
+- dot_map@100
+- query_active_dims
+- query_sparsity_ratio
+- corpus_active_dims
+- corpus_sparsity_ratio
+model-index:
+- name: Inference-free SPLADE distilbert-base-uncased trained on Natural-Questions
+    tuples
+  results:
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: NanoMSMARCO
+      type: NanoMSMARCO
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.3
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.58
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.66
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.76
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.3
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.19333333333333336
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.132
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.07600000000000001
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.3
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.58
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.66
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.76
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.5302210774188797
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.45638095238095233
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.4675385567218492
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 6.380000114440918
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.9997909704437966
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 813.6908569335938
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9733408408055306
+      name: Corpus Sparsity Ratio
+---
+
+# Inference-free SPLADE distilbert-base-uncased trained on Natural-Questions tuples
+
+This is a [Asymmetric Inference-free SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space   and can be used for semantic search and sparse retrieval.
+## Model Details
+
+### Model Description
+- **Model Type:** Asymmetric Inference-free SPLADE Sparse Encoder
+- **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 30522 dimensions
+- **Similarity Function:** Dot Product
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### 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/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
+
+### Full Model Architecture
+
+```
+SparseEncoder(
+  (0): Router(
+    (sub_modules): ModuleDict(
+      (query): Sequential(
+        (0): SparseStaticEmbedding({'frozen': False}, dim=30522, tokenizer=DistilBertTokenizerFast)
+      )
+      (document): Sequential(
+        (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'DistilBertForMaskedLM'})
+        (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
+      )
+    )
+  )
+)
+```
+
+## 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("monkeypostulate/inference-free-splade-distilbert-base-uncased-nq")
+# Run inference
+queries = [
+    "\u00bfHay una s\u00e1bana de algod\u00f3n ajustada disponible en tama\u00f1o queen?",
+]
+documents = [
+    'Pizuna 400 Thread Count Cotton Fitted-Sheet Queen Size White 1pc, 100% Long Staple Cotton Sateen Fitted Bed Sheet With All Around Elastic Deep Pocket Queen Sheets Fit Up to 15Inch (White Fitted Sheet)',
+    'ArtSocket Shower Curtain Teal Rustic Shabby Country Chic Blue Curtains Wood Rose Home Bathroom Decor Polyester Fabric Waterproof 72 x 72 Inches Set with Hooks',
+    'AFARER Case Compatible with Samsung Galaxy S7 5.1 inch, Military Grade 12ft Drop Tested Protective Case with Kickstand,Military Armor Dual Layer Protective Cover - Blue',
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# [1, 30522] [3, 30522]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+# tensor([[13.2777,  7.2952,  2.9255]])
+```
+
+<!--
+### 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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Sparse Information Retrieval
+
+* Dataset: `NanoMSMARCO`
+* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator)
+
+| Metric                | Value      |
+|:----------------------|:-----------|
+| dot_accuracy@1        | 0.3        |
+| dot_accuracy@3        | 0.58       |
+| dot_accuracy@5        | 0.66       |
+| dot_accuracy@10       | 0.76       |
+| dot_precision@1       | 0.3        |
+| dot_precision@3       | 0.1933     |
+| dot_precision@5       | 0.132      |
+| dot_precision@10      | 0.076      |
+| dot_recall@1          | 0.3        |
+| dot_recall@3          | 0.58       |
+| dot_recall@5          | 0.66       |
+| dot_recall@10         | 0.76       |
+| **dot_ndcg@10**       | **0.5302** |
+| dot_mrr@10            | 0.4564     |
+| dot_map@100           | 0.4675     |
+| query_active_dims     | 6.38       |
+| query_sparsity_ratio  | 0.9998     |
+| corpus_active_dims    | 813.6909   |
+| corpus_sparsity_ratio | 0.9733     |
+
+<!--
+## 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
+
+#### Unnamed Dataset
+
+* Size: 89,000 training samples
+* Columns: <code>query</code> and <code>document</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                             | document                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 21.52 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 33.4 tokens</li><li>max: 93 tokens</li></ul> |
+* Samples:
+  | query                                                              | document                                                                                                                                                                            |
+  |:-------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>¿Hay una lámpara de colgar con batería disponible?</code>    | <code>Farmhouse Plug in Pendant Light with On/Off Switch Wire Caged Hanging Pendant Lamp 16ft Cord</code>                                                                           |
+  | <code>¿Hay leggings con bolsillos disponibles para mujeres?</code> | <code>IUGA High Waist Yoga Pants with Pockets, Tummy Control, Workout Pants for Women 4 Way Stretch Yoga Leggings with Pockets</code>                                               |
+  | <code>¿Cuál es la tapa de oscuridad marrón disponible?</code>      | <code>Thicken It 100% Scalp Coverage Hair Powder - DARK BROWN - Talc-Free .32 oz. Water Resistant Hair Loss Concealer. Naturally Thicker Than Hair Fibers & Spray Concealers</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)",
+      "document_regularizer_weight": 0.003,
+      "query_regularizer_weight": 0
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 1,000 evaluation samples
+* Columns: <code>query</code> and <code>document</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                             | document                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 20.94 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 33.09 tokens</li><li>max: 79 tokens</li></ul> |
+* Samples:
+  | query                                                                          | document                                                                                                                                                                                                             |
+  |:-------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>¿Qué es un modelo anatómico del corazón?</code>                          | <code>Axis Scientific Heart Model, 2-Part Deluxe Life Size Human Heart Replica with 34 Anatomical Structures, Held Together with Magnets, Includes Mounted Display Base, Detailed Product Manual and Warranty</code> |
+  | <code>¿Hay un buscador de peces portátil disponible?</code>                    | <code>HawkEye Fishtrax 1C Fish Finder with HD Color Virtuview Display, Black/Red, 2" H x 1.6" W Screen Size</code>                                                                                                   |
+  | <code>¿Hay un disfraz de Anna adulta de Frozen disponible para comprar?</code> | <code>Mitef Anime Cosplay Costume Princess Anna Fancy Dress with Shawl for Adult, L</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)",
+      "document_regularizer_weight": 0.003,
+      "query_regularizer_weight": 0
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `learning_rate`: 2e-05
+- `warmup_ratio`: 0.1
+- `batch_sampler`: no_duplicates
+- `router_mapping`: {'query': 'query', 'answer': 'document'}
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `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
+- `use_legacy_prediction_loop`: False
+- `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_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `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`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {'query': 'query', 'answer': 'document'}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | NanoMSMARCO_dot_ndcg@10 |
+|:------:|:----:|:-------------:|:-----------------------:|
+| 0.5747 | 200  | 3.33          | -                       |
+| 1.1494 | 400  | 2.755         | -                       |
+| -1     | -1   | -             | 0.5302                  |
+
+
+### Framework Versions
+- Python: 3.9.6
+- Sentence Transformers: 5.1.0
+- Transformers: 4.55.0
+- PyTorch: 2.8.0
+- Accelerate: 1.10.0
+- Datasets: 4.0.0
+- Tokenizers: 0.21.4
+
+## 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{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+#### 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.*
+--> 

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SparseEncoder",
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.55.0",
+    "pytorch": "2.8.0"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "dot"
+}

document_0_MLMTransformer/config.json

@@ -0,0 +1,23 @@
+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForMaskedLM"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.0",
+  "vocab_size": 30522
+}

document_0_MLMTransformer/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be0e44d5d6bbf9e553d89e57c178f6c1b539962df9ab0d8e8bbe584a576f7555
+size 267954768

document_0_MLMTransformer/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

document_0_MLMTransformer/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

document_0_MLMTransformer/tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

document_1_SpladePooling/config.json

@@ -0,0 +1,5 @@
+{
+    "pooling_strategy": "max",
+    "activation_function": "relu",
+    "word_embedding_dimension": 30522
+}

modules.json

@@ -0,0 +1,8 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Router"
+  }
+]

query_0_SparseStaticEmbedding/config.json

@@ -0,0 +1,3 @@
+{
+    "frozen": false
+}

query_0_SparseStaticEmbedding/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ebdac66a6b9e9ca8e50bfaa3191bc4b6a88f0b1d1f2bb6c2f7346138efa7b5f
+size 122168

query_0_SparseStaticEmbedding/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

query_0_SparseStaticEmbedding/tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

router_config.json

@@ -0,0 +1,20 @@
+{
+    "types": {
+        "query_0_SparseStaticEmbedding": "sentence_transformers.sparse_encoder.models.SparseStaticEmbedding.SparseStaticEmbedding",
+        "document_0_MLMTransformer": "sentence_transformers.sparse_encoder.models.MLMTransformer.MLMTransformer",
+        "document_1_SpladePooling": "sentence_transformers.sparse_encoder.models.SpladePooling.SpladePooling"
+    },
+    "structure": {
+        "query": [
+            "query_0_SparseStaticEmbedding"
+        ],
+        "document": [
+            "document_0_MLMTransformer",
+            "document_1_SpladePooling"
+        ]
+    },
+    "parameters": {
+        "default_route": "document",
+        "allow_empty_key": true
+    }
+}