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---
base_model: sentence-transformers/all-MiniLM-L6-v2
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1053
- loss:CosineSimilarityLoss
widget:
- source_sentence: 'question: Radiateur électrique à inertie fluide pas cher disponible
à Bastia ? ----->query: query=radiateur électrique inertie fluide&sort=price-asc&context=298'
sentences:
- 'question: Je recherche un pied de table disponible dans le magasin d''Ivry sur
Seine. ----->query: query=Pied de table&context=142'
- 'question: Peinture intérieure Luxens disponible dans le magasin de Vitry ? ----->query:
query=luxens peinture interieure&context=21'
- 'question: Radiateur disponible dans le magasin de Montauban ? ----->query: query=Radiateur&context=189'
- source_sentence: 'question: Avez-vous des produits bio ? ----->query: query=Bio'
sentences:
- 'question: Je cherche des parpaings creux disponibles dans le magasin de Pau. ----->query:
query=parpaing creux&context=41'
- 'question: Je recherche des profilés disponibles dans le magasin de Bordeaux. ----->query:
query=profilé&context=37'
- 'question: Avez-vous des supports collecteurs disponibles dans le magasin de Strasbourg
? ----->query: query=Support collecteur&context=40'
- source_sentence: 'question: Donnez-moi les pieds de table les moins chers disponibles
dans le magasin de Thoiry. ----->query: query=pieds table&sort=price-asc&context=167'
sentences:
- 'question: Je cherche des pieds pour meuble. ----->query: query=Pieds meuble'
- 'question: J''ai besoin d''enduit de rebouchage pour un chantier, est-ce que vous
en avez en stock dans le magasin d''Osny ? ----->query: query=enduit de rebouchage&context=23'
- 'question: Avez-vous du mastic d''étanchéité disponible dans le magasin de Clermont
Ferrand ? ----->query: query=mastic d''etancheite&context=133'
- source_sentence: 'question: Donnez-moi les pieds de table les moins chers disponibles
dans le magasin de Thoiry. ----->query: query=pieds table&sort=price-asc&context=167'
sentences:
- 'question: Je recherche du parquet. ----->query: query=parket'
- 'question: J''aimerais savoir si vous avez des pinces à dénuder dans le magasin
de Cabries. ----->query: query=pince a denuder&context=66'
- 'question: Parquet contrecollé pas cher dans le magasin de Nice. ----->query:
query=parquet contrecolle&sort=price-asc&context=6'
- source_sentence: 'question: Je cherche une scie dans le magasin de Dinard. ----->query:
query=Scie&context=178'
sentences:
- 'question: Dalles pour l''extérieur ----->query: query=dalle exterieur'
- 'question: J''ai besoin d''une goulotte pour câble électrique, disponible dans
le magasin de Vitry. ----->query: query=goulotte pour cable electrique&context=21'
- 'question: J''aimerais savoir si vous avez des pinces à dénuder dans le magasin
de Cabries. ----->query: query=pince a denuder&context=66'
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/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': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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): Normalize()
)
```
## 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("yandac/embedding_model_search_api")
# Run inference
sentences = [
'question: Je cherche une scie dans le magasin de Dinard. ----->query: query=Scie&context=178',
"question: J'aimerais savoir si vous avez des pinces à dénuder dans le magasin de Cabries. ----->query: query=pince a denuder&context=66",
"question: J'ai besoin d'une goulotte pour câble électrique, disponible dans le magasin de Vitry. ----->query: query=goulotte pour cable electrique&context=21",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 1,053 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 20 tokens</li><li>mean: 45.16 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 43.69 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.24</li><li>max: 0.9</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>question: Peinture pour bois extérieur disponible dans le magasin de Mundolsheim ? ----->query: query=Peinture bois extérieur&context=197</code> | <code>question: Avez-vous des plans de travail d'angle disponibles dans le magasin de Douai ? ----->query: query=plan de travail d'angle&context=183</code> | <code>0.0</code> |
| <code>question: Sac de granulés de bois disponible dans le magasin de Brive ? ----->query: query=sac granule bois&context=175</code> | <code>question: Avez-vous des 1/2 ronds disponibles dans le magasin de Compiegne ? ----->query: query=1/2 rond&context=78</code> | <code>0.0</code> |
| <code>question: Je cherche un rouleau d'étanchéité disponible dans le magasin de Cabries. ----->query: query=rouleau etancheite&context=66</code> | <code>question: Je recherche un pied de table disponible dans le magasin d'Ivry sur Seine. ----->query: query=Pied de table&context=142</code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 1
- `num_train_epochs`: 4.8
- `warmup_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 1
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-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`: 4.8
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: 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`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `dispatch_batches`: None
- `split_batches`: 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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 1.5152 | 100 | 0.0071 |
| 0.4748 | 500 | 0.0076 |
| 0.9497 | 1000 | 0.0162 |
| 1.4245 | 1500 | 0.0164 |
| 1.8993 | 2000 | 0.0155 |
| 2.3742 | 2500 | 0.0112 |
| 2.8490 | 3000 | 0.0106 |
| 3.3238 | 3500 | 0.0064 |
| 3.7987 | 4000 | 0.0055 |
| 4.2735 | 4500 | 0.0043 |
| 4.7483 | 5000 | 0.0027 |
| 0.4748 | 500 | 0.0046 |
| 0.9497 | 1000 | 0.0102 |
| 1.4245 | 1500 | 0.0134 |
| 1.8993 | 2000 | 0.0133 |
| 2.3742 | 2500 | 0.0086 |
| 2.8490 | 3000 | 0.007 |
| 3.3238 | 3500 | 0.0049 |
| 3.7987 | 4000 | 0.0037 |
| 4.2735 | 4500 | 0.0031 |
| 4.7483 | 5000 | 0.0022 |
### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu118
- Accelerate: 0.33.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## 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",
}
```
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