---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:605748
- loss:MultipleNegativesSymmetricRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: sand eel shad soft lure combo eelo 150 25 g ayu/blue
  sentences:
  - marvel na! na! na! surprise 2-pack air arms multicolor
  - fast fishing fishing lure
  - fishing
- source_sentence: rosa / porcelain us andalusia mug
  sentences:
  - ramadan mug
  - mug
  - song plant dracaena reflexa shade
- source_sentence: apple cinnamon greek yoghurt
  sentences:
  - dairy
  - low sugar yogurt
  - moko milk chocolate 33 % no sugar added
- source_sentence: rembrandt's eyes
  sentences:
  - art book
  - penguin uk books
  - farm coloring book
- source_sentence: faber castell jumbo colored pencil, metallic copper
  sentences:
  - squirrel machine for forming creative clay
  - ' colored pencil'
  - pencil
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: 0.9442633390426636
      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-V17Data-128BATCH-SemanticEngine")
# Run inference
sentences = [
    'faber castell jumbo colored pencil, metallic copper',
    ' colored pencil',
    'squirrel machine for forming creative clay',
]
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, 0.7595, 0.1715],
#         [0.7595, 1.0000, 0.2370],
#         [0.1715, 0.2370, 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** | **0.9443** |

<!--
## 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: 605,748 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: 9.0 tokens</li><li>max: 133 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.16 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.93 tokens</li><li>max: 9 tokens</li></ul> |
* Samples:
  | anchor                                            | positive                                            | itemCategory                   |
  |:--------------------------------------------------|:----------------------------------------------------|:-------------------------------|
  | <code>wipeable nylon suitcase</code>              | <code>accessories</code>                            | <code>bag</code>               |
  | <code>kids light and flexible riptab shoes</code> | <code>comfy trainers for running and jumping</code> | <code>sports shoe</code>       |
  | <code>sugarlo 50mg 30tab2exnew</code>             | <code>sugarlo</code>                                | <code>diabetes medicine</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: 6.14 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.1 tokens</li><li>max: 43 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> pencil </code>                      | <code>thermal food bag coral high 5 l 1 zipper 11812 camouflage dinosaur</code> | <code>pencil</code>                 |
  | <code>superior drawing marker -pen - set of 12 colors - 2 nib</code> | <code> nib marker pen</code>               | <code>modeling clay block 550 gr black</code>                                   | <code>marker</code>                 |
  | <code>first person singular author: haruki murakami</code>           | <code>penguin random house usa book</code> | <code>blue colada</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`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 5
- `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-V17Data-128BATCH-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`: 128
- `per_device_eval_batch_size`: 128
- `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`: 2e-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`: 5
- `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-V17Data-128BATCH-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 |
|:------:|:-----:|:-------------:|:---------------:|:---------------:|
| 0.0002 | 1     | 2.6278        | -               | -               |
| 0.2113 | 1000  | 2.7281        | 0.6126          | 0.9346          |
| 0.4226 | 2000  | 1.999         | 0.5676          | 0.9389          |
| 0.6338 | 3000  | 1.5073        | 0.5519          | 0.9328          |
| 0.8451 | 4000  | 0.9687        | 0.5425          | 0.9312          |
| 1.0564 | 5000  | 0.9492        | 0.5104          | 0.9442          |
| 1.2677 | 6000  | 1.3563        | 0.5167          | 0.9431          |
| 1.4790 | 7000  | 1.253         | 0.5245          | 0.9433          |
| 1.6903 | 8000  | 0.9613        | 0.5144          | 0.9373          |
| 1.9015 | 9000  | 0.6725        | 0.5081          | 0.9388          |
| 2.1128 | 10000 | 0.8854        | 0.4964          | 0.9442          |
| 2.3241 | 11000 | 1.0927        | 0.4986          | 0.9469          |
| 2.5354 | 12000 | 1.0451        | 0.4878          | 0.9465          |
| 2.7467 | 13000 | 0.7421        | 0.4899          | 0.9421          |
| 2.9580 | 14000 | 0.5394        | 0.4943          | 0.9391          |
| 3.1692 | 15000 | 0.9123        | 0.4896          | 0.9456          |
| 3.3805 | 16000 | 0.9725        | 0.4869          | 0.9486          |
| 3.5918 | 17000 | 0.9007        | 0.4895          | 0.9445          |
| 3.8031 | 18000 | 0.6232        | 0.4809          | 0.9443          |


### 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.*
-->