---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:790756
- loss:MultipleNegativesSymmetricRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: creamy black varnish for black leathers
  sentences:
  - shoe accessory
  - the first product scented, nourishing, polishing and preserving all types of leather
    50 gr.
  - steal the scene t-shirt
- source_sentence: beige lounge set
  sentences:
  - pajamas
  - women pajama set
  - not so basic sports bra
- source_sentence: not not donner
  sentences:
  - sesame bites
  - stuffed dough
  - deli
- source_sentence: seaboat-5 240/2 sea fishing combo
  sentences:
  - fishing
  - vertical fishing rod
  - small pool ball - red
- source_sentence: eva a.bacterial h.sanitizer han.gel350m#
  sentences:
  - blue balloon collection
  - sanitizer
  - ' hand gel'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy
      value: 0.9748573899269104
      name: Cosine Accuracy
---

# 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 c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **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/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': 256, 'do_lower_case': False, 'architecture': '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("LamaDiab/MiniLM-v35-SemanticEngine")
# Run inference
sentences = [
    'eva a.bacterial h.sanitizer han.gel350m#',
    ' hand gel',
    'blue balloon collection',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.4571, -0.0845],
#         [ 0.4571,  1.0000,  0.0257],
#         [-0.0845,  0.0257,  1.0000]])
```

<!--
### 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

#### Triplet

* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9749** |

<!--
## 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: 790,756 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>itemCategory</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                         | itemCategory                                                                    |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | string                                                                          |
  | details | <ul><li>min: 3 tokens</li><li>mean: 8.91 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.92 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.95 tokens</li><li>max: 9 tokens</li></ul> |
* Samples:
  | anchor                                                         | positive                                                 | itemCategory                   |
  |:---------------------------------------------------------------|:---------------------------------------------------------|:-------------------------------|
  | <code>m&g acrylic marker, apl976d966, viridescent, s500</code> | <code>m&g acrylic marker, apl976d966, green, s500</code> | <code>marker</code>            |
  | <code>daky raspberry 48h deo r.on 2x50m@#</code>               | <code>deodorant</code>                                   | <code>women's deodorant</code> |
  | <code>melatex sun yellow spf(50+)50m</code>                    | <code>melatex cream spf(50+)50m</code>                   | <code>skin whitening</code>    |
* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset

* Size: 9,466 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, and <code>itemCategory</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                         | negative                                                                         | itemCategory                                                                    |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | string                                                                           | string                                                                          |
  | details | <ul><li>min: 3 tokens</li><li>mean: 9.65 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 6.0 tokens</li><li>max: 131 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.08 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.82 tokens</li><li>max: 9 tokens</li></ul> |
* Samples:
  | anchor                                                 | positive                     | negative                                                      | itemCategory          |
  |:-------------------------------------------------------|:-----------------------------|:--------------------------------------------------------------|:----------------------|
  | <code>extra bubblemint sugar free chewing gum</code>   | <code> gum</code>            | <code>zumra coconut milk 17-19% fats</code>                   | <code>sweet</code>    |
  | <code>golden pothos</code>                             | <code>evergreen plant</code> | <code>stainless steel insulated hiking bottle 1 l blue</code> | <code>plant</code>    |
  | <code>effortless style slit linen pants - beige</code> | <code>women pants</code>     | <code>cool grey camouflage training short sleeve top</code>   | <code>trousers</code> |
* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `learning_rate`: 3e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `fp16`: True
- `dataloader_num_workers`: 1
- `dataloader_prefetch_factor`: 2
- `dataloader_persistent_workers`: True
- `push_to_hub`: True
- `hub_model_id`: LamaDiab/MiniLM-v35-SemanticEngine
- `hub_strategy`: all_checkpoints

#### 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`: 256
- `per_device_eval_batch_size`: 256
- `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`: 3e-05
- `weight_decay`: 0.01
- `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`: 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`: 1
- `dataloader_prefetch_factor`: 2
- `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`: True
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: LamaDiab/MiniLM-v35-SemanticEngine
- `hub_strategy`: all_checkpoints
- `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 | Validation Loss | cosine_accuracy |
|:------:|:-----:|:-------------:|:---------------:|:---------------:|
| 0.0003 | 1     | 2.5131        | -               | -               |
| 0.3237 | 1000  | 1.8415        | 1.0824          | 0.9512          |
| 0.6475 | 2000  | 1.3696        | 0.9929          | 0.9617          |
| 0.9712 | 3000  | 1.4502        | 0.9487          | 0.9656          |
| 1.2947 | 4000  | 1.3141        | 0.8925          | 0.9704          |
| 1.6182 | 5000  | 1.1692        | 0.8781          | 0.9709          |
| 1.9418 | 6000  | 1.1209        | 0.8579          | 0.9718          |
| 2.2653 | 7000  | 1.0609        | 0.8649          | 0.9738          |
| 2.5888 | 8000  | 1.0507        | 0.8569          | 0.9725          |
| 2.9123 | 9000  | 1.0079        | 0.8493          | 0.9736          |
| 3.2358 | 10000 | 1.0006        | 0.8392          | 0.9735          |
| 3.5594 | 11000 | 0.9947        | 0.8390          | 0.9751          |
| 3.8829 | 12000 | 0.9774        | 0.8403          | 0.9749          |


### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 5.1.2
- Transformers: 4.53.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.9.0
- Datasets: 4.4.1
- Tokenizers: 0.21.2

## 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",
}
```

<!--
## 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.*
-->