---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:705905
- loss:MultipleNegativesSymmetricRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: gerber baby food fruits apples bananas & cereal
  sentences:
  - world of sweets puzzle
  - baby food
  - baby food
- source_sentence: granville original one bite original rice crispy squares
  sentences:
  - ' one bite rice crispy '
  - sweet
  - bounty wafer rolls
- source_sentence: rosa / porcelain us andalusia mug
  sentences:
  - mug
  - ' rosa mug'
  - melamine small plate - teal
- source_sentence: cetaphil sunscreen spf 50+ cream 89 ml
  sentences:
  - sunscreen
  - ' cetaphil sunscreen cream'
  - garnier intensity (6.60) intense ruby
- source_sentence: italian dolce provolone
  sentences:
  - trident - gum strawberry flavor - 5 per pack
  - experience the authentic taste of italy with our italian dolce provolone. indulge
    in its creamy texture, delicate flavors, and versatility in both simple and sophisticated
    culinary creations.
  - dairy
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: 0.9646650552749634
      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-V18Data-256ConstantBATCH-SemanticEngine")
# Run inference
sentences = [
    'italian dolce provolone',
    'experience the authentic taste of italy with our italian dolce provolone. indulge in its creamy texture, delicate flavors, and versatility in both simple and sophisticated culinary creations.',
    'trident - gum strawberry flavor - 5 per pack',
]
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, 0.8581, 0.2671],
#         [0.8581, 1.0000, 0.2847],
#         [0.2671, 0.2847, 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** | **0.9647** |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 705,905 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: 13.19 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.46 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.91 tokens</li><li>max: 11 tokens</li></ul> |
* Samples:
  | anchor                                         | positive                                 | itemCategory                   |
  |:-----------------------------------------------|:-----------------------------------------|:-------------------------------|
  | <code>mango nos  nos small</code>              | <code>milk chocolate ganache cake</code> | <code>sweet</code>             |
  | <code>lux soap creamy perfection 165 gm</code> | <code>soap</code>                        | <code>hand soap</code>         |
  | <code>grey deo original</code>                 | <code>classic deodrant</code>            | <code>women's deodorant</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: 9,509 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: 9.63 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 6.53 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.52 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.88 tokens</li><li>max: 10 tokens</li></ul> |
* Samples:
  | anchor                                                               | positive                          | negative                                                                                       | itemCategory                        |
  |:---------------------------------------------------------------------|:----------------------------------|:-----------------------------------------------------------------------------------------------|:------------------------------------|
  | <code>pilot mechanical pencil progrex h-127 - 0.7 mm</code>          | <code>office supplies</code>      | <code>scary halloween skull mask</code>                                                        | <code>pencil</code>                 |
  | <code>superior drawing marker -pen - set of 12 colors - 2 nib</code> | <code>superior </code>            | <code>coloring and writing book 21 x 29.7 cm 100 gsm 18 pages number subtraction ma4014</code> | <code>marker</code>                 |
  | <code>first person singular author: haruki murakami</code>           | <code>haruki murakami book</code> | <code>buried secrets</code>                                                                    | <code>literature and fiction</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`: 1e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 6
- `warmup_ratio`: 0.2
- `fp16`: True
- `dataloader_num_workers`: 1
- `dataloader_prefetch_factor`: 2
- `dataloader_persistent_workers`: True
- `push_to_hub`: True
- `hub_model_id`: LamaDiab/MiniLM-V18Data-256ConstantBATCH-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`: 1e-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`: 6
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `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-V18Data-256ConstantBATCH-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 |
|:------:|:-----:|:-------------:|:---------------:|:---------------:|
| 0.0004 | 1     | 4.1707        | -               | -               |
| 0.3626 | 1000  | 3.7074        | 0.5848          | 0.9430          |
| 0.7252 | 2000  | 2.5733        | 0.5230          | 0.9468          |
| 1.0877 | 3000  | 2.1499        | 0.4858          | 0.9546          |
| 1.4503 | 4000  | 2.3929        | 0.4693          | 0.9578          |
| 1.8129 | 5000  | 1.6541        | 0.4415          | 0.9597          |
| 2.1755 | 6000  | 1.8335        | 0.4474          | 0.9615          |
| 2.5381 | 7000  | 1.839         | 0.4331          | 0.9625          |
| 2.9007 | 8000  | 1.3238        | 0.4197          | 0.9624          |
| 3.2632 | 9000  | 1.8409        | 0.4281          | 0.9647          |
| 3.6258 | 10000 | 1.511         | 0.4207          | 0.9653          |
| 3.9884 | 11000 | 1.1623        | 0.4108          | 0.9647          |


### 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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