add SPLADE v6_v2 (qwen30-pairs A/B winner, sparse nDCG 0.8189)

splade_v6_v2/1_SpladePooling/config.json

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

splade_v6_v2/README.md

@@ -0,0 +1,497 @@
+---
+tags:
+- sentence-transformers
+- sparse-encoder
+- sparse
+- splade
+- generated_from_trainer
+- dataset_size:1298215
+- loss:SpladeLoss
+- loss:SparseMultipleNegativesRankingLoss
+- loss:FlopsLoss
+base_model: distilbert/distilbert-base-multilingual-cased
+widget:
+- text: '[Clan London] Women''s Logo Tote Bag Olive (Olive) | حقيبة توت بشعار العلامة
+    التجارية للنساء، زيتي (زيتي). Category: Bags > Shoppers & Totes.'
+- text: '[Fire & Glory] Women''s Animal Print Midi Dress Light Gray (Light Gray) |
+    فستان ميدي بأكمام طويلة وطبعة جرافيك للنساء، أسود (رمادي فاتح). Category: Dresses
+    > Midi Dresses.'
+- text: '[melissa] Melissa Becky AD Pink (Pink) | ميليسا بيكي إيه دي (وردي). Category:
+    Shoes > Slides & Flip-Flops.'
+- text: '[H&M] Curvy Ultra High-Rise Wide-Leg Jeans (Pastel Blue) | جينز كيرفي بخصر
+    مرتفع جداً وأرجل واسعة (أزرق باستيل). Category: Denim > Jeans.'
+- text: '[Splash Basics] Flexi Comfort Drawstring A-line Midi Skirt Blue (Blue) |
+    الرباط ألف خط تنورة ميدي (أزرق). Category: Bottoms > Skirts.'
+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 pairs and triplets datasets 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:** 128 tokens
+- **Output Dimensionality:** 119547 dimensions
+- **Similarity Function:** Dot Product
+- **Supported Modality:** Text
+- **Training Datasets:**
+    - pairs
+    - triplets
+<!-- - **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 = [
+    'شنطة زهّانة',
+    '[PANDORA] Aquarius Zodiac Charm (Silver) | دلاية برج الدلو (فضي). Category:  > Accessories.',
+    "[Hurley] Women's Crew Neck Solid Sweatshirt Dark Salmon (Dark Salmon) | سويت شيرت نسائي سادة بياقة دائرية، برتقالي (سلموني غامق). Category: Hoodies & Sweatshirt > Sweatshirts.",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 119547]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[ 11.9930,  13.0235,   7.8521],
+#         [ 13.0235, 491.0651,  65.4858],
+#         [  7.8521,  65.4858, 246.8788]])
+```
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Datasets
+
+#### pairs
+
+* Dataset: pairs
+* Size: 1,292,307 training samples
+* Columns: <code>query</code> and <code>positive</code>
+* Approximate statistics based on the first 100 samples:
+  |          | query                                                                             | positive                                                                            |
+  |:---------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type     | string                                                                            | string                                                                              |
+  | modality | text                                                                              | text                                                                                |
+  | details  | <ul><li>min: 6 tokens</li><li>mean: 10.33 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 51.14 tokens</li><li>max: 124 tokens</li></ul> |
+* Samples:
+  | query                           | positive                                                                                                                                                                      |
+  |:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>men's watch analog</code> | <code>[CASIO] Leather Strap Analog Watch (Mint Green) \| ساعة انالوج جلد اصلي (أخضر نعناعي). Category:  > .</code>                                                            |
+  | <code>black hoodie women</code> | <code>[Tribe of 6] Women's Logo Hooded Sweatshirt Black (Black) \| سويت شيرت نسائي سادة بأكمام طويلة وغطاء رأس، أسود (أسود). Category: Hoodies & Sweatshirt > Hoodies.</code> |
+  | <code>وشاح مخطط للنساء</code>   | <code>[MANGO] Geometric Stripe Scarf Beige (Beige) \| وشاح بنقوش هندسية مخططة (بيج). Category: Accessories > Scarves.</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
+  }
+  ```
+
+#### triplets
+
+* Dataset: triplets
+* Size: 5,908 training samples
+* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 100 samples:
+  |          | query                                                                            | positive                                                                           | negative                                                                           |
+  |:---------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type     | string                                                                           | string                                                                             | string                                                                             |
+  | modality | text                                                                             | text                                                                               | text                                                                               |
+  | details  | <ul><li>min: 5 tokens</li><li>mean: 8.75 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 57.33 tokens</li><li>max: 82 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 54.78 tokens</li><li>max: 88 tokens</li></ul> |
+* Samples:
+  | query                                                     | positive                                                                                                                                                                         | negative                                                                                                                                                                                        |
+  |:----------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>don't call me jennyfer jumpsuits & bodysuits</code> | <code>[Don't Call Me Jennyfer] Women's One Sleeve Bodysuit Green (Green) \| بودي سوت سادة بأكمام قصيرة للنساء، أخضر (أخضر). Category: Jumpsuits & Bodysuits > Body Suits.</code> | <code>[Ashita Fernandes] Kenza Cupro V-Neck Jumpsuit Royal Blue (Facebook Blue) \| كينزا جمبسوت كوبرو بفتحة رقبة V أزرق ملكي (أزرق فيسبوك). Category: Jumpsuits & Bodysuits > Jumpsuits.</code> |
+  | <code>don't call me jennyfer jumpsuits & bodysuits</code> | <code>[Don't Call Me Jennyfer] Women's One Sleeve Bodysuit Green (Green) \| بودي سوت سادة بأكمام قصيرة للنساء، أخضر (أخضر). Category: Jumpsuits & Bodysuits > Body Suits.</code> | <code>[Splash Intimates] Mesh Panel Shaping Bodysuit (Black) \| بودي سوت مشد بأجزاء شبكية (أسود). Category: Jumpsuits & Bodysuits > Body Suits.</code>                                          |
+  | <code>don't call me jennyfer jumpsuits & bodysuits</code> | <code>[Don't Call Me Jennyfer] Women's One Sleeve Bodysuit Green (Green) \| بودي سوت سادة بأكمام قصيرة للنساء، أخضر (أخضر). Category: Jumpsuits & Bodysuits > Body Suits.</code> | <code>[Ashita Fernandes] Kenza Cupro V-Neck Jumpsuit Royal Blue (Facebook Blue) \| كينزا جمبسوت كوبرو بفتحة رقبة V أزرق ملكي (أزرق فيسبوك). Category: Jumpsuits & Bodysuits > Jumpsuits.</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
+- `warmup_steps`: 0.1
+- `gradient_accumulation_steps`: 4
+- `bf16`: True
+- `dataloader_num_workers`: 2
+
+#### 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`: 0.1
+- `optim`: adamw_torch
+- `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`: True
+- `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`: None
+- `trackio_bucket_id`: None
+- `trackio_static_space_id`: None
+- `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`: 2
+- `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_static_graph`: None
+- `ddp_backend`: None
+- `ddp_timeout`: 1800
+- `fsdp`: None
+- `fsdp_config`: None
+- `deepspeed`: None
+- `debug`: []
+- `skip_memory_metrics`: True
+- `do_predict`: False
+- `resume_from_checkpoint`: None
+- `warmup_ratio`: None
+- `local_rank`: -1
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0099 | 200   | 679.9589      |
+| 0.0197 | 400   | 1.7857        |
+| 0.0296 | 600   | 1.0238        |
+| 0.0394 | 800   | 0.8982        |
+| 0.0493 | 1000  | 0.8221        |
+| 0.0592 | 1200  | 0.7875        |
+| 0.0690 | 1400  | 0.7405        |
+| 0.0789 | 1600  | 0.7131        |
+| 0.0887 | 1800  | 0.6761        |
+| 0.0986 | 2000  | 0.6732        |
+| 0.1085 | 2200  | 0.6397        |
+| 0.1183 | 2400  | 0.6303        |
+| 0.1282 | 2600  | 0.6021        |
+| 0.1380 | 2800  | 0.5794        |
+| 0.1479 | 3000  | 0.5552        |
+| 0.1578 | 3200  | 0.5638        |
+| 0.1676 | 3400  | 0.5633        |
+| 0.1775 | 3600  | 0.5550        |
+| 0.1873 | 3800  | 0.5473        |
+| 0.1972 | 4000  | 0.5281        |
+| 0.2070 | 4200  | 0.5189        |
+| 0.2169 | 4400  | 0.5282        |
+| 0.2268 | 4600  | 0.5080        |
+| 0.2366 | 4800  | 0.5046        |
+| 0.2465 | 5000  | 0.5111        |
+| 0.2563 | 5200  | 0.4957        |
+| 0.2662 | 5400  | 0.4932        |
+| 0.2761 | 5600  | 0.5045        |
+| 0.2859 | 5800  | 0.5019        |
+| 0.2958 | 6000  | 0.4789        |
+| 0.3056 | 6200  | 0.4866        |
+| 0.3155 | 6400  | 0.4810        |
+| 0.3254 | 6600  | 0.4891        |
+| 0.3352 | 6800  | 0.4800        |
+| 0.3451 | 7000  | 0.4659        |
+| 0.3549 | 7200  | 0.4612        |
+| 0.3648 | 7400  | 0.4644        |
+| 0.3747 | 7600  | 0.4364        |
+| 0.3845 | 7800  | 0.4672        |
+| 0.3944 | 8000  | 0.4546        |
+| 0.4042 | 8200  | 0.4631        |
+| 0.4141 | 8400  | 0.4565        |
+| 0.4240 | 8600  | 0.4242        |
+| 0.4338 | 8800  | 0.4425        |
+| 0.4437 | 9000  | 0.4597        |
+| 0.4535 | 9200  | 0.4292        |
+| 0.4634 | 9400  | 0.4406        |
+| 0.4733 | 9600  | 0.4331        |
+| 0.4831 | 9800  | 0.4342        |
+| 0.4930 | 10000 | 0.4483        |
+| 0.5028 | 10200 | 0.4254        |
+| 0.5127 | 10400 | 0.4182        |
+| 0.5226 | 10600 | 0.4173        |
+| 0.5324 | 10800 | 0.4120        |
+| 0.5423 | 11000 | 0.4197        |
+| 0.5521 | 11200 | 0.4029        |
+| 0.5620 | 11400 | 0.4016        |
+| 0.5719 | 11600 | 0.4159        |
+| 0.5817 | 11800 | 0.4033        |
+| 0.5916 | 12000 | 0.4250        |
+| 0.6014 | 12200 | 0.4013        |
+| 0.6113 | 12400 | 0.4234        |
+| 0.6211 | 12600 | 0.4103        |
+| 0.6310 | 12800 | 0.4046        |
+| 0.6409 | 13000 | 0.4123        |
+| 0.6507 | 13200 | 0.4074        |
+| 0.6606 | 13400 | 0.4098        |
+| 0.6704 | 13600 | 0.4192        |
+| 0.6803 | 13800 | 0.3887        |
+| 0.6902 | 14000 | 0.4017        |
+| 0.7000 | 14200 | 0.3956        |
+| 0.7099 | 14400 | 0.3971        |
+| 0.7197 | 14600 | 0.3964        |
+| 0.7296 | 14800 | 0.3921        |
+| 0.7395 | 15000 | 0.3947        |
+| 0.7493 | 15200 | 0.3795        |
+| 0.7592 | 15400 | 0.3813        |
+| 0.7690 | 15600 | 0.3931        |
+| 0.7789 | 15800 | 0.3792        |
+| 0.7888 | 16000 | 0.3878        |
+| 0.7986 | 16200 | 0.3703        |
+| 0.8085 | 16400 | 0.3836        |
+| 0.8183 | 16600 | 0.3871        |
+| 0.8282 | 16800 | 0.3751        |
+| 0.8381 | 17000 | 0.4005        |
+| 0.8479 | 17200 | 0.3887        |
+| 0.8578 | 17400 | 0.3873        |
+| 0.8676 | 17600 | 0.3811        |
+| 0.8775 | 17800 | 0.3841        |
+| 0.8874 | 18000 | 0.3841        |
+| 0.8972 | 18200 | 0.3701        |
+| 0.9071 | 18400 | 0.3913        |
+| 0.9169 | 18600 | 0.3848        |
+| 0.9268 | 18800 | 0.3885        |
+| 0.9367 | 19000 | 0.3648        |
+| 0.9465 | 19200 | 0.3842        |
+| 0.9564 | 19400 | 0.3989        |
+| 0.9662 | 19600 | 0.3930        |
+| 0.9761 | 19800 | 0.3944        |
+| 0.9860 | 20000 | 0.3712        |
+| 0.9958 | 20200 | 0.3762        |
+
+</details>
+
+### Training Time
+- **Training**: 48.5 minutes
+
+### Framework Versions
+- Python: 3.11.12
+- Sentence Transformers: 5.6.0
+- Transformers: 5.12.1
+- PyTorch: 2.7.0+cu128
+- Accelerate: 1.14.0
+- Datasets: 5.0.0
+- 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.*
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+
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+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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splade_v6_v2/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",
+  "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": "5.12.1",
+  "use_cache": false,
+  "vocab_size": 119547
+}

splade_v6_v2/config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "pytorch": "2.7.0+cu128",
+    "sentence_transformers": "5.6.0",
+    "transformers": "5.12.1"
+  },
+  "default_prompt_name": null,
+  "model_type": "SparseEncoder",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": null
+}

splade_v6_v2/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:011f6d4142e86a7c2509572f5e07be4d89c6c3eed532e603b8cbcf04e73c4bf2
+size 541795684

splade_v6_v2/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"
+  }
+]

splade_v6_v2/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"
+}

splade_v6_v2/tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 128,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}