add fine-tuned SPLADE v4 (occasion fix)

splade_v4/1_SpladePooling/config.json

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

splade_v4/README.md

@@ -0,0 +1,443 @@
+---
+tags:
+- sentence-transformers
+- sparse-encoder
+- sparse
+- splade
+- generated_from_trainer
+- dataset_size:633174
+- loss:SpladeLoss
+- loss:SparseMultipleNegativesRankingLoss
+- loss:FlopsLoss
+base_model: distilbert/distilbert-base-multilingual-cased
+widget:
+- text: '[Styli] Wired Floral Lace Balconette Bra Pink (Pink) | حمالة صدر بالكونيت
+    دانتيل بتطريز زهري وحواف مقوسة بدون حشوة وسلكية مع لوحة مقصوصة مخيطة (وردي). Category:
+    Lingerie & Underwear > Bras.'
+- text: '[SHAPES] OVERSIZED COMFORT HOODIE (Black) | هودي مريح كبير الحجم (أسود).
+    Category:  > .'
+- text: '[Splash] Linen Blend Polo T-shirt Blue (Blue) | Linen Blend Polo T-shirt
+    (أزرق). Category: Tops > Shirts & Button-Downs.'
+- text: '[CAMPUS] Women''s Pastel Low Top Sneakers Beige (Beige) | أحذية رياضية نسائية
+    قصيرة بتدرجات الباستيل البارزة (بيج). Category: Shoes > Sneakers.'
+- text: '[Karl Lagerfeld] Women''s Long Sleeve Printed Top Black (Black) | بلوزة مطبوعة
+    بالكامل بأكمام طويلة ورقبة دائرية للنساء، متعددة الألوان (أسود). Category:  >
+    .'
+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 = [
+    'كندرة طولية مطبوعة رجالي',
+    "[Karl Lagerfeld] Women's Long Sleeve Printed Top Black (Black) | بلوزة مطبوعة بالكامل بأكمام طويلة ورقبة دائرية للنساء، متعددة الألوان (أسود). Category:  > .",
+    '[Splash Fav] Regular Fit Twill Blazer Cream (Cream) | Regular Fit Twill Blazer with Button Closure (كريمي). Category: Suits > Blazers.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 119547]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[ 30.1986,  30.1103,  15.3038],
+#         [ 30.1103, 193.7718,  59.9433],
+#         [ 15.3038,  59.9433, 248.5168]])
+```
+<!--
+### 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: 605,436 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: 27,738 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: 11 tokens</li><li>mean: 12.15 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 48.58 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 52.9 tokens</li><li>max: 62 tokens</li></ul> |
+* Samples:
+  | query                                 | positive                                                                                                                              | negative                                                                                                                                                                     |
+  |:--------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>كندرة أونلاين بسروات زين</code> | <code>[Khizana] Denim A-line Skirt Steel Blue (Steel Blue) \| تنورة - من الدنيم (أزرق فولاذي). Category: Denim > Denim Skirts.</code> | <code>[Tchibo] Women's Checkered Pull-On Shorts Dark Blue (Dark Blue) \| شورت بنمط مربعات سهل الارتداء للنساء، أزرق (أزرق غامق). Category:  > .</code>                       |
+  | <code>كندرة أونلاين بسروات زين</code> | <code>[Khizana] Denim A-line Skirt Steel Blue (Steel Blue) \| تنورة - من الدنيم (أزرق فولاذي). Category: Denim > Denim Skirts.</code> | <code>[Styli] Styli Set of 2 Ribbon Detail Socks Light Steel Blue (Light Steel Blue) \| طقم جوارب من قطعتين بتفاصيل شريط (أزرق فولاذي فاتح). Category: Shoes > Socks.</code> |
+  | <code>كندرة أونلاين بسروات زين</code> | <code>[Khizana] Denim A-line Skirt Steel Blue (Steel Blue) \| تنورة - من الدنيم (أزرق فولاذي). Category: Denim > Denim Skirts.</code> | <code>[Tchibo] Women's Checkered Pull-On Shorts Dark Blue (Dark Blue) \| شورت بنمط مربعات سهل الارتداء للنساء، أزرق (أزرق غامق). Category:  > .</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
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0202 | 200  | 518.0875      |
+| 0.0404 | 400  | 1.3120        |
+| 0.0606 | 600  | 1.0065        |
+| 0.0809 | 800  | 0.9330        |
+| 0.1011 | 1000 | 0.8807        |
+| 0.1213 | 1200 | 0.8054        |
+| 0.1415 | 1400 | 0.7627        |
+| 0.1617 | 1600 | 0.7404        |
+| 0.1819 | 1800 | 0.7277        |
+| 0.2022 | 2000 | 0.7013        |
+| 0.2224 | 2200 | 0.6515        |
+| 0.2426 | 2400 | 0.6359        |
+| 0.2628 | 2600 | 0.6334        |
+| 0.2830 | 2800 | 0.6284        |
+| 0.3032 | 3000 | 0.6302        |
+| 0.3234 | 3200 | 0.5996        |
+| 0.3437 | 3400 | 0.5933        |
+| 0.3639 | 3600 | 0.5846        |
+| 0.3841 | 3800 | 0.5783        |
+| 0.4043 | 4000 | 0.5907        |
+| 0.4245 | 4200 | 0.5669        |
+| 0.4447 | 4400 | 0.5768        |
+| 0.4650 | 4600 | 0.5507        |
+| 0.4852 | 4800 | 0.5403        |
+| 0.5054 | 5000 | 0.5372        |
+| 0.5256 | 5200 | 0.5382        |
+| 0.5458 | 5400 | 0.5308        |
+| 0.5660 | 5600 | 0.5453        |
+| 0.5862 | 5800 | 0.5218        |
+| 0.6065 | 6000 | 0.5058        |
+| 0.6267 | 6200 | 0.5229        |
+| 0.6469 | 6400 | 0.5245        |
+| 0.6671 | 6600 | 0.5115        |
+| 0.6873 | 6800 | 0.5023        |
+| 0.7075 | 7000 | 0.5049        |
+| 0.7278 | 7200 | 0.5155        |
+| 0.7480 | 7400 | 0.4976        |
+| 0.7682 | 7600 | 0.4965        |
+| 0.7884 | 7800 | 0.4968        |
+| 0.8086 | 8000 | 0.4950        |
+| 0.8288 | 8200 | 0.4889        |
+| 0.8490 | 8400 | 0.4918        |
+| 0.8693 | 8600 | 0.4870        |
+| 0.8895 | 8800 | 0.4723        |
+| 0.9097 | 9000 | 0.4808        |
+| 0.9299 | 9200 | 0.4791        |
+| 0.9501 | 9400 | 0.4829        |
+| 0.9703 | 9600 | 0.4654        |
+| 0.9905 | 9800 | 0.4931        |
+
+
+### Training Time
+- **Training**: 37.1 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.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+--> 

splade_v4/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_v4/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_v4/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9d6772d1f7cb5b61ab380c7cc3a917e3efd0fe8a70898cc207410e8fae9c8ac
+size 541795684

splade_v4/modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "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_v4/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_v4/tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "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]"
+}