Add new SparseEncoder model

1_SpladePooling/config.json

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

README.md

@@ -0,0 +1,509 @@
+---
+tags:
+- sentence-transformers
+- sparse-encoder
+- sparse
+- splade
+- generated_from_trainer
+- dataset_size:1200000
+- loss:SpladeLoss
+- loss:SparseMarginMSELoss
+- loss:FlopsLoss
+base_model: yosefw/SPLADE-BERT-Small-BS128
+widget:
+- text: Donate to the Breast Cancer Research Foundation Now BCRF is the largest nonprofit
+    funder of breast cancer research worldwide. Over the years, it has raised more
+    than half a billion dollars in support of research that has made a major impact
+    on how we view and treat breast cancer.
+- text: Macular degeneration—Loss of central vision, blurred vision (especially while
+    reading), distorted vision (like seeing wavy lines), and colors appearing faded.
+    The most common cause of blindness in people over age 60. Eye infection, inflammation,
+    or injury.
+- text: how do i find the tongue weight of a trailer?
+- text: Feathers (1-3) Pidgey are docile Pokémon, and generally prefer to flee from
+    their enemies rather than fight them. Pidgey's small size permits it to hide easily
+    in long grass, where it is typically found foraging for small insects. It is known
+    to flush out potential prey from long grass by flapping its wings rapidly.
+- text: 10 hilariously insightful foreign words. One of the most obvious differences
+    between cognac and whiskey is that cognac makers use grapes, and whiskey makers
+    use grains. Although both processes use fermentation to create the liquors, cognac
+    makers use a double distillation process.
+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: SPLADE Sparse Encoder
+  results:
+  - task:
+      type: sparse-information-retrieval
+      name: Sparse Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: dot_accuracy@1
+      value: 0.5172
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.8368
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.9232
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.9762
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.5172
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.2866666666666667
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.1924
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.10273999999999998
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.5006
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.8237833333333332
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.91535
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.9723333333333332
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.7553714776897319
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.6876940476190507
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.6829029994536953
+      name: Dot Map@100
+    - type: query_active_dims
+      value: 29.71980094909668
+      name: Query Active Dims
+    - type: query_sparsity_ratio
+      value: 0.9990262826502491
+      name: Query Sparsity Ratio
+    - type: corpus_active_dims
+      value: 168.3538420216879
+      name: Corpus Active Dims
+    - type: corpus_sparsity_ratio
+      value: 0.9944841805248121
+      name: Corpus Sparsity Ratio
+---
+
+# SPLADE Sparse Encoder
+
+This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [yosefw/SPLADE-BERT-Small-BS128](https://huggingface.co/yosefw/SPLADE-BERT-Small-BS128) 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:** SPLADE Sparse Encoder
+- **Base model:** [yosefw/SPLADE-BERT-Small-BS128](https://huggingface.co/yosefw/SPLADE-BERT-Small-BS128) <!-- at revision 27575d2504e7400b5ed11f94d0e162e3e7c01af6 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 30522 dimensions
+- **Similarity Function:** Dot Product
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **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/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): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertForMaskedLM'})
+  (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("yosefw/SPLADE-BERT-Small-BS128-distil")
+# Run inference
+queries = [
+    "is cognac whisky",
+]
+documents = [
+    'Cognac vs Whiskey. • Whiskey is the alcoholic drink made from grains whereas Cognac is the alcoholic drink made from grapes. • Cognac is a type of brandy. In fact, many label it as the finest of brandies. • Cognac is the brandy originating from a wine producing region of France called Cognac. • While a cognac is considered an after dinner beverage that is intended to digest food, there is no such stereotyping of whiskey that can be consumed anytime of the day.',
+    '10 hilariously insightful foreign words. One of the most obvious differences between cognac and whiskey is that cognac makers use grapes, and whiskey makers use grains. Although both processes use fermentation to create the liquors, cognac makers use a double distillation process.',
+    'The word whisky (or whiskey) is an anglicisation of the Classical Gaelic word uisce / uisge meaning water (now written as uisce in Irish Gaelic, and uisge in Scottish Gaelic). Distilled alcohol was known in Latin as aqua vitae (water of life).',
+]
+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([[22.4589, 20.5905, 10.0662]])
+```
+
+<!--
+### 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
+
+* 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        | 0.5172     |
+| dot_accuracy@3        | 0.8368     |
+| dot_accuracy@5        | 0.9232     |
+| dot_accuracy@10       | 0.9762     |
+| dot_precision@1       | 0.5172     |
+| dot_precision@3       | 0.2867     |
+| dot_precision@5       | 0.1924     |
+| dot_precision@10      | 0.1027     |
+| dot_recall@1          | 0.5006     |
+| dot_recall@3          | 0.8238     |
+| dot_recall@5          | 0.9153     |
+| dot_recall@10         | 0.9723     |
+| **dot_ndcg@10**       | **0.7554** |
+| dot_mrr@10            | 0.6877     |
+| dot_map@100           | 0.6829     |
+| query_active_dims     | 29.7198    |
+| query_sparsity_ratio  | 0.999      |
+| corpus_active_dims    | 168.3538   |
+| corpus_sparsity_ratio | 0.9945     |
+
+<!--
+## 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: 1,200,000 training samples
+* Columns: <code>query</code>, <code>positive</code>, <code>negative_1</code>, <code>negative_2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                            | positive                                                                            | negative_1                                                                          | negative_2                                                                         | label                              |
+  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------|
+  | type    | string                                                                           | string                                                                              | string                                                                              | string                                                                             | list                               |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 9.04 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 81.11 tokens</li><li>max: 215 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 77.81 tokens</li><li>max: 247 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 76.2 tokens</li><li>max: 217 tokens</li></ul> | <ul><li>size: 2 elements</li></ul> |
+* Samples:
+  | query                                                                                                                                | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | negative_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | negative_2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | label                                                 |
+  |:-------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------|
+  | <code>The _____________________ is a body system which consists of glands that produce hormones that act throughout the body.</code> | <code>Endocrine System. The endocrine system is made up of a group of glands that produce the body's long-distance messengers, or hormones. Hormones are chemicals that control body functions, such as metabolism, growth, and sexual development.t is made up of a group of organs that transport blood throughout the body. The heart pumps the blood and the arteries and veins transport it. Oxygen-rich blood leaves the left side of the heart and enters the biggest artery, called the aorta.</code>                                                                                                      | <code>The endocrine system is a control system of ductless glands that secrete hormones within specific organs. Hormones act as messengers, and are carried by the bloodstream to different cells in the body, which interpret these messages and act on them.he pancreas is unusual among the body's glands in that it also has a very important endocrine function. Small groups of special cells called islet cells throughout the organ make the hormones of insulin and glucagon.</code> | <code>These glands produce different types of hormones that evoke a specific response in other cells, tissues, and/or organs located throughout the body. The hormones reach these faraway targets using the blood stream. Like the nervous system, the endocrine system is one of your body’s main communicators.he Endocrine System Essentials. 1  The endocrine system is made up of a network of glands. 2  These glands secrete hormones to regulate many bodily functions, including growth and metabolism.</code> | <code>[2.3722684383392334, 5.211579322814941]</code>  |
+  | <code>causes of low body temperature in adults</code>                                                                                | <code>Hypothermia is defined as a body temperature (core, or internal body temperature) of less than about 95 F (35 C). Usually, hypothermia occurs when the body's temperature regulation is overwhelmed by a cold environment. However, in the medical and lay literature there are essentially two major classifications, accidental hypothermia and intentional hypothermia.</code>                                                                                                                                                                                                                            | <code>In general, a baby has a fever when their body temperature exceeds 100.4°F, or 38°C. A child has a fever when their temperature exceeds 99.5°F, or 37.5°C. An adult has a fever when their temperature exceeds 99 to 99.5°F, or 37.2 to 37.5°C.</code>                                                                                                                                                                                                                                  | <code>Consequently, an accurate measurement of body temperature (best is rectal core temperature) of 100.4 F (38 C) or above is considered to be a fever.. A newer option includes a temperature-sensitive infrared device that measures the temperature in the skin by simply rubbing the sensor on the body.</code>                                                                                                                                                                                                    | <code>[1.3747079372406006, 8.096447944641113]</code>  |
+  | <code>who is laila gifty akita</code>                                                                                                | <code>Lailah Gifty Akita is a Ghanaian and founder of Smart Youth Volunteers Foundation. She obtained a BSc in Renewable Natural Resources Management at Kwame Nkrumah University of Science and Technology, Kumasi-Ghana. She also had MPhil in Oceanography at the University of Ghana. She obtained a doctorate in Geosciences at International Max Planck Research School for Global Biogeochemical Cycles-Friedrich Schiller University of Jena, Germany ( June 2011 to March 2016). Lailah is an influential lady with the passion of empowering the mind of young people to make a great difference.</code> | <code>She is a PhD-student, studying Geosciences at the University of Jena, Germany. She is an enthusiastic inspirational writer. She wishes to challenge and inspire people from all walks of life to dare a greater life. You can capable of heroic deeds. Think well of yourself and act positively. You can correspond with Lailah via an email:[email protected]. https://www.goodreads.com/author/show/8297615.Lailah_Gifty_Akita/blog.</code>                                    | <code>Also in the Talmud, the interpretation is found of rabbi Hanina ben Pappa (3rd century AD), that Lailah is an angel in charge of conception who takes a drop of semen and places it before God, saying: For R. Hanina b.</code>                                                                                                                                                                                                                                                                                    | <code>[2.6488447189331055, 15.058775901794434]</code> |
+* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
+  ```json
+  {
+      "loss": "SparseMarginMSELoss",
+      "document_regularizer_weight": 0.12,
+      "query_regularizer_weight": 0.2
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `learning_rate`: 4e-05
+- `num_train_epochs`: 4
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.025
+- `fp16`: True
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `push_to_hub`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `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`: 4e-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`: 4
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.025
+- `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`: True
+- `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`: True
+- `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`: True
+- `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`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch   | Step      | Training Loss | dot_ndcg@10 |
+|:-------:|:---------:|:-------------:|:-----------:|
+| 1.0     | 18750     | 7.806         | 0.7439      |
+| 2.0     | 37500     | 5.7509        | 0.7520      |
+| **3.0** | **56250** | **4.5026**    | **0.7554**  |
+| 4.0     | 75000     | 3.909         | 0.7534      |
+| -1      | -1        | -             | 0.7554      |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.11.13
+- Sentence Transformers: 5.1.0
+- Transformers: 4.55.2
+- PyTorch: 2.6.0+cu124
+- 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},
+}
+```
+
+#### SparseMarginMSELoss
+```bibtex
+@misc{hofstätter2021improving,
+    title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
+    author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury},
+    year={2021},
+    eprint={2010.02666},
+    archivePrefix={arXiv},
+    primaryClass={cs.IR}
+}
+```
+
+#### 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.json

@@ -0,0 +1,24 @@
+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 512,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 4,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SparseEncoder",
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.55.2",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "dot"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6ec00e9e7df6c50634dc89a9e490f1f787ef35fd9beccce17895323138acbedc
+size 115189296

modules.json

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

sentence_bert_config.json

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

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}