---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:902990
- loss:MultipleNegativesSymmetricRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: dove deodorant stick fresh
  sentences:
  - women's deodorant
  - antiperspirant deodorant stick
  - bubbles natur.bottle w.hand pink(6)m280m
- source_sentence: avrelle travel size olive oil & rocca set
  sentences:
  - haircare set
  - shampoo
  - maldives waterfall body splash
- source_sentence: not not donner
  sentences:
  - mix chocolate pie
  - stuffed dough with donner, mozzarella cheese, tomatoes, lettuce, onion with ranch
    sauce.
  - deli
- source_sentence: pink n' yellow
  sentences:
  - mirror
  - yellow mirror
  - watercolor pan 2.5 ml scarlet semi white nights no 1911318
- source_sentence: ice cream roll machine for kids
  sentences:
  - horse pink
  - pretend play
  - chocolates game
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: 0.9772871136665344
      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-v32-SemanticEngine")
# Run inference
sentences = [
    'ice cream roll machine for kids',
    'chocolates game',
    'horse pink',
]
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,  0.3049, -0.0634],
#         [ 0.3049,  1.0000,  0.0119],
#         [-0.0634,  0.0119,  1.0000]])
```

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## 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** | **0.9773** |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 902,990 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: 10.31 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.38 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.01 tokens</li><li>max: 9 tokens</li></ul> |
* Samples:
  | anchor                                               | positive                   | itemCategory                      |
  |:-----------------------------------------------------|:---------------------------|:----------------------------------|
  | <code>adult skipping rope - petrol</code>            | <code>skipping rope</code> | <code>gym equipment</code>        |
  | <code>bambino rainbow gold & sliver sage</code>      | <code>shisha</code>        | <code>home decor accessory</code> |
  | <code>joobran’s minimalist clutch – evergreen</code> | <code>crossbody bag</code> | <code>bag</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: 5.75 tokens</li><li>max: 131 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.32 tokens</li><li>max: 36 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>dried mangoes</code>                              | <code>sweet</code>    |
  | <code>golden pothos</code>                             | <code>indoor plant</code> | <code>fire truck gam</code>                             | <code>plant</code>    |
  | <code>effortless style slit linen pants - beige</code> | <code>beige pants</code>  | <code>light grey keep going stone washed t-shirt</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-v32-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-v32-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     | 2.6239        | -               | -               |
| 0.2834 | 1000  | 2.239         | 1.0706          | 0.9631          |
| 0.5669 | 2000  | 1.6315        | 0.9980          | 0.9705          |
| 0.8503 | 3000  | 1.4885        | 0.8850          | 0.9736          |
| 1.1337 | 4000  | 1.8836        | 0.8782          | 0.9750          |
| 1.4170 | 5000  | 1.3749        | 0.8663          | 0.9754          |
| 1.7003 | 6000  | 1.3631        | 0.8465          | 0.9769          |
| 1.9836 | 7000  | 1.3027        | 0.8421          | 0.9772          |
| 2.2669 | 8000  | 1.2536        | 0.8478          | 0.9765          |
| 2.5501 | 9000  | 1.2155        | 0.8358          | 0.9767          |
| 2.8334 | 10000 | 1.1847        | 0.8319          | 0.9782          |
| 3.1167 | 11000 | 1.1857        | 0.8344          | 0.9778          |
| 3.4    | 12000 | 1.1544        | 0.8378          | 0.9773          |
| 3.6833 | 13000 | 1.1787        | 0.8315          | 0.9771          |
| 3.9666 | 14000 | 1.1452        | 0.8320          | 0.9773          |


### 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",
}
```

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