---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:713598
- loss:MultipleNegativesSymmetricRankingLoss
base_model: LamaDiab/MiniLM-V23Data-256ConstantBATCH-SemanticEngine
widget:
- source_sentence: must kindergarten backpack mermazing 2 cases
  sentences:
  - 100 horse riding sleeveless gilet - black
  - ' must backpack '
  - bag
- source_sentence: elephant ear alocasia
  sentences:
  - alocasia plant
  - plant
  - blue happy birthday napkins
- source_sentence: dove antiperspirant deodorant, pomegranate & lemon verbena scent
  sentences:
  - '"womens deodorant"'
  - ' pomegranate deodorant'
  - women's windproof and water-repellent hiking jacket - raincut 1/2 zip
- source_sentence: pizza pepperoni
  sentences:
  - deli
  - ' pizza'
  - diet shish tawook plain
- source_sentence: y earrings
  sentences:
  - slate short-sleeved t-shirt
  - circles earrings
  - earring
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on LamaDiab/MiniLM-V23Data-256ConstantBATCH-SemanticEngine
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy
      value: 0.9647701978683472
      name: Cosine Accuracy
---

# SentenceTransformer based on LamaDiab/MiniLM-V23Data-256ConstantBATCH-SemanticEngine

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [LamaDiab/MiniLM-V23Data-256ConstantBATCH-SemanticEngine](https://huggingface.co/LamaDiab/MiniLM-V23Data-256ConstantBATCH-SemanticEngine). 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:** [LamaDiab/MiniLM-V23Data-256ConstantBATCH-SemanticEngine](https://huggingface.co/LamaDiab/MiniLM-V23Data-256ConstantBATCH-SemanticEngine) <!-- at revision faa16e0abfe5428205c9a58333948bd4139ec818 -->
- **Maximum Sequence Length:** 128 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': 128, '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/FinetuningMiniLM-V23Data-256ConstantBATCH-SemanticEngine")
# Run inference
sentences = [
    'y earrings',
    'circles earrings',
    'slate short-sleeved t-shirt',
]
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,  0.9446, -0.0041],
#         [ 0.9446,  1.0000,  0.0491],
#         [-0.0041,  0.0491,  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** | **0.9648** |

<!--
## 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: 713,598 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: 10.85 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.49 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.89 tokens</li><li>max: 9 tokens</li></ul> |
* Samples:
  | anchor                                          | positive              | itemCategory          |
  |:------------------------------------------------|:----------------------|:----------------------|
  | <code>almond bark chocolate</code>              | <code>sweet</code>    | <code>sweet</code>    |
  | <code>beige wide leg cotton fleece pants</code> | <code>trousers</code> | <code>trousers</code> |
  | <code>spice guru paprika powder</code>          | <code>pantry</code>   | <code>pantry</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: 3 tokens</li><li>mean: 6.43 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.31 tokens</li><li>max: 41 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> mechanical pencil </code>     | <code>daler rowney smooth sketchbook, a5, 130 gsm, 30 sheets, 403010500, french</code> | <code>pencil</code>                 |
  | <code>superior drawing marker -pen - set of 12 colors - 2 nib</code> | <code>superior drawing marker</code> | <code>fc albrecht dürer pencil no. 124</code>                                          | <code>marker</code>                 |
  | <code>first person singular author: haruki murakami</code>           | <code>haruki murakami book</code>    | <code>sarab</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
- `weight_decay`: 0.001
- `num_train_epochs`: 2
- `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/FinetuningMiniLM-V23Data-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`: 5e-05
- `weight_decay`: 0.001
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `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/FinetuningMiniLM-V23Data-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    | 1.248         | -               | -               |
| 0.3587 | 1000 | 1.223         | 0.4691          | 0.9626          |
| 0.7174 | 2000 | 0.8193        | 0.4478          | 0.9604          |
| 1.0760 | 3000 | 0.6847        | 0.4490          | 0.9651          |
| 1.4344 | 4000 | 0.7661        | 0.4400          | 0.9646          |
| 1.7928 | 5000 | 0.7319        | 0.4436          | 0.9648          |


### 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.*
-->