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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:102127
- loss:SpladeLoss
- loss:SparseMultipleNegativesRankingLoss
- loss:FlopsLoss
base_model: seregadgl/splade_gemma_google_base_checkpoint_100_clear
widget:
- source_sentence: 'query: 6460338 acdelco'
sentences:
- 'document: очиститель тормозов rsqprofessional арт 072589767pl volkswagen id buzz
янтарный'
- 'document: гтц 6460338 для chevrolet traverse'
- 'document: гтц 6960358 для chevrolet traverse'
- source_sentence: 'query: audioquest cinnamon usb 0 7500 см '
sentences:
- 'document: кабель usb аудиоквест cinnamon 0 7500 см 8712516'
- 'document: задняя камера рамке номерного знака интерпауэр ip616 54785862'
- 'document: аудиокабель soundwave 200 см'
- source_sentence: 'query: акустическое пианино weber w 121 pw '
sentences:
- 'document: акустическое пианино steinway model s'
- 'document: инструмент для игры на пианино вебер w 121 pw'
- 'document: велосипед сильвербек strela sport 700c 54 см blue 60097000435025'
- source_sentence: 'query: шкаф шрм24'
sentences:
- 'document: wardrobe shrm 24 4348563'
- 'document: духовой шкаф бертаццони f6011provtn'
- 'document: шкаф мдф30'
- source_sentence: 'query: 1452634 santool jawa 300 cl'
sentences:
- 'document: смартфон эппл iphone xs max 512gb'
- 'document: 1453934 santool съемник для сальников jawa 300 cl'
- 'document: 1452634 santool съемник для сальников jawa 300 cl'
datasets:
- seregadgl/car_and_product_triplet_103k
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on seregadgl/splade_gemma_google_base_checkpoint_100_clear
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: val set fine
type: val_set_fine
metrics:
- type: cosine_accuracy@1
value: 0.742
name: Cosine Accuracy@1
- type: cosine_precision@1
value: 0.742
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27633333333333326
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1728
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08910000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.742
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.829
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.864
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.891
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8160719769563038
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7919432539682544
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7955622385483846
name: Cosine Map@100
---
# SentenceTransformer based on seregadgl/splade_gemma_google_base_checkpoint_100_clear
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [seregadgl/splade_gemma_google_base_checkpoint_100_clear](https://huggingface.co/seregadgl/splade_gemma_google_base_checkpoint_100_clear) on the [car_and_product_triplet_103k](https://huggingface.co/datasets/seregadgl/car_and_product_triplet_103k) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [seregadgl/splade_gemma_google_base_checkpoint_100_clear](https://huggingface.co/seregadgl/splade_gemma_google_base_checkpoint_100_clear) <!-- at revision 20c38a098901bc44c1031a7537d0e3bf0aa93063 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [car_and_product_triplet_103k](https://huggingface.co/datasets/seregadgl/car_and_product_triplet_103k)
<!-- - **Language:** Unknown -->
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### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): SparseLayer(
(linear): Linear(in_features=768, out_features=262144, bias=True)
)
)
```
## 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 SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("seregadgl/splade_gemma_google_base_checkpoint_100_ver2")
# Run inference
sentences = [
'query: 1452634 santool jawa 300 cl',
'document: 1452634 santool съемник для сальников jawa 300 cl',
'document: 1453934 santool съемник для сальников jawa 300 cl',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.1443, 0.1452],
# [0.1443, 1.0000, 0.7490],
# [0.1452, 0.7490, 1.0000]])
```
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You can finetune this model on your own dataset.
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## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `val_set_fine`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.742 |
| cosine_precision@1 | 0.742 |
| cosine_precision@3 | 0.2763 |
| cosine_precision@5 | 0.1728 |
| cosine_precision@10 | 0.0891 |
| cosine_recall@1 | 0.742 |
| cosine_recall@3 | 0.829 |
| cosine_recall@5 | 0.864 |
| cosine_recall@10 | 0.891 |
| **cosine_ndcg@10** | **0.8161** |
| cosine_mrr@10 | 0.7919 |
| cosine_map@100 | 0.7956 |
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## Training Details
### Training Dataset
#### car_and_product_triplet_103k
* Dataset: [car_and_product_triplet_103k](https://huggingface.co/datasets/seregadgl/car_and_product_triplet_103k) at [3519181](https://huggingface.co/datasets/seregadgl/car_and_product_triplet_103k/tree/35191818e272dc373544bd86903a5146c6f993e2)
* Size: 102,127 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 16.27 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 23.62 tokens</li><li>max: 77 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 23.2 tokens</li><li>max: 47 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| <code>query: погружной блендер tefal optichef hb64f810</code> | <code>document: погружной блендер тефаль optichef hb64f810</code> | <code>document: погружной миксер tefal mixchef hb64f850</code> |
| <code>query: 375675836 niteo</code> | <code>document: тосол 375675836 для ford f350 полуночный синий</code> | <code>document: тосол 375625836 для ford f350 полуночный синий фиалковый</code> |
| <code>query: накидка с подогревом dodge viper pink</code> | <code>document: накидка с подогревом acdelco арт 787327sx dodge viper розовый</code> | <code>document: 787327sx накидка с подогревом indian challenger лаймовый</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)",
"document_regularizer_weight": 1e-05,
"query_regularizer_weight": 1e-05
}
```
### Evaluation Dataset
#### car_and_product_triplet_103k
* Dataset: [car_and_product_triplet_103k](https://huggingface.co/datasets/seregadgl/car_and_product_triplet_103k) at [3519181](https://huggingface.co/datasets/seregadgl/car_and_product_triplet_103k/tree/35191818e272dc373544bd86903a5146c6f993e2)
* Size: 1,000 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 16.73 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 23.54 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 22.66 tokens</li><li>max: 65 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| <code>query: зеркала для 'слепых' зон volkswagen arteon</code> | <code>document: зеркала для 'слепых' зон 86635985zz для volkswagen arteon перламутровочёрный</code> | <code>document: 86635985zz зеркала для 'слепых' зон иж юпитер2 голубой</code> |
| <code>query: elf bar lux 1500 лимонад голубой малины 1500 </code> | <code>document: одноразовая электронная сигарета эльф бар 1 5000 мл lemonade blue raspberry 340440526</code> | <code>document: elf bar vibe 1000 мохито зелёного яблока 1000</code> |
| <code>query: удалитель наклеек chevrolet corvette onyx</code> | <code>document: удалитель наклеек 20810588pl для chevrolet corvette оникс</code> | <code>document: удалитель наклеек 20810588pl для maserati levante янтарный</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)",
"document_regularizer_weight": 1e-05,
"query_regularizer_weight": 1e-05
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `gradient_accumulation_steps`: 16
- `learning_rate`: 0.0001
- `num_train_epochs`: 1
- `warmup_steps`: 10
- `fp16`: True
- `load_best_model_at_end`: True
- `router_mapping`: {'query': 'anchor', 'document': 'positive'}
#### 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`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.0001
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 10
- `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
- `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}
- `parallelism_config`: 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
- `project`: huggingface
- `trackio_space_id`: trackio
- `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`: no
- `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`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {'query': 'anchor', 'document': 'positive'}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Validation Loss | val_set_fine_cosine_ndcg@10 |
|:----------:|:------:|:---------------:|:---------------------------:|
| 0.0125 | 10 | 0.8461 | 0.7841 |
| 0.0251 | 20 | 0.8195 | 0.8009 |
| 0.0376 | 30 | 0.7884 | 0.7967 |
| 0.0501 | 40 | 0.7641 | 0.8097 |
| **0.0627** | **50** | **0.7503** | **0.8146** |
| 0.0752 | 60 | 0.7140 | 0.8151 |
| 0.0877 | 70 | 0.7165 | 0.8180 |
| 0.1003 | 80 | 0.6955 | 0.8131 |
| 0.1128 | 90 | 0.6866 | 0.8157 |
| 0.1253 | 100 | 0.6735 | 0.8170 |
| 0.1379 | 110 | 0.6766 | 0.8159 |
| 0.1504 | 120 | 0.6609 | 0.8161 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.2.2
- Transformers: 4.57.1
- PyTorch: 2.8.0+cu126
- Accelerate: 1.11.0
- Datasets: 4.4.2
- Tokenizers: 0.22.1
## 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}
}
```
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