README.md

---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:25110
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: APPLE iPhone 16 PRO MAX 512GB
  sentences:
  - Communications Devices and Accessories
  - Communications Devices and Accessories
  - Communications Devices and Accessories
- source_sentence: CISCO.CISCO 878-K9 G.SHDSL SECURİTY ROUTER
  sentences:
  - Communications Devices and Accessories
  - Data Voice or Multimedia Network Equipment or Platforms and Accessories
  - Computer Equipment and Accessories
- source_sentence: iPhone 14 36 months Tier 3+
  sentences:
  - Heating and ventilation and air circulation
  - Portable Structure Building Components
  - Components for information technology or broadcasting or telecommunications
- source_sentence: Elektrik Sayacı Optik Okuyucu
  sentences:
  - Components for information technology or broadcasting or telecommunications
  - Power sources
  - Components for information technology or broadcasting or telecommunications
- source_sentence: Power Cable,600V/1000V,ROV-K,4mm^2,Black Jacket(The Color Of Core
    Is Blue And Brown),36A,Shielded Style Outdoor Cable
  sentences:
  - Electrical equipment and components and supplies
  - Communications Devices and Accessories
  - Power sources
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: pearson_cosine
      value: .nan
      name: Pearson Cosine
    - type: spearman_cosine
      value: .nan
      name: Spearman Cosine
    - type: pearson_manhattan
      value: .nan
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: .nan
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: .nan
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: .nan
      name: Spearman Euclidean
    - type: pearson_dot
      value: .nan
      name: Pearson Dot
    - type: spearman_dot
      value: .nan
      name: Spearman Dot
    - type: pearson_max
      value: .nan
      name: Pearson Max
    - type: spearman_max
      value: .nan
      name: Spearman Max
---

# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 768-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision f1b1b820e405bb8644f5e8d9a3b98f9c9e0a3c58 -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 tokens
- **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/UKPLab/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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, '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("alpcansoydas/product-model-16.10.24-ifhavemorethan10sampleperfamily")
# Run inference
sentences = [
    'Power Cable,600V/1000V,ROV-K,4mm^2,Black Jacket(The Color Of Core Is Blue And Brown),36A,Shielded Style Outdoor Cable',
    'Electrical equipment and components and supplies',
    'Power sources',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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## Evaluation

### Metrics

#### Semantic Similarity

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

| Metric             | Value   |
|:-------------------|:--------|
| pearson_cosine     | nan     |
| spearman_cosine    | nan     |
| pearson_manhattan  | nan     |
| spearman_manhattan | nan     |
| pearson_euclidean  | nan     |
| spearman_euclidean | nan     |
| pearson_dot        | nan     |
| spearman_dot       | nan     |
| pearson_max        | nan     |
| **spearman_max**   | **nan** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 25,110 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                        |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           |
  | details | <ul><li>min: 3 tokens</li><li>mean: 17.04 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.97 tokens</li><li>max: 12 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                                        | sentence2                                                                            |
  |:---------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | <code>USRC20(RH2288,2*E5-2680v2,16*16G,12*600GB(2.5 )+2*600GB(2.5 ),4*10GE,4*GE,DC)-OS RAID1,DATA RAID5+Hotspare,No DVDRW</code> | <code>Computer Equipment and Accessories</code>                                      |
  | <code>100m 160x10 Kafes Kule</code>                                                                                              | <code>Heavy construction machinery and equipment</code>                              |
  | <code>Air4820 Superonline Video Bridge</code>                                                                                    | <code>Data Voice or Multimedia Network Equipment or Platforms and Accessories</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 5,381 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                        |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           |
  | details | <ul><li>min: 3 tokens</li><li>mean: 16.75 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.89 tokens</li><li>max: 12 tokens</li></ul> |
* Samples:
  | sentence1                                                         | sentence2                                                                                |
  |:------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|
  | <code>SNTC-24X7X4 Cisco ISR 4331 (2GE,2NIM,4G FLASH,4G DRA</code> | <code>Data Voice or Multimedia Network Equipment or Platforms and Accessories</code>     |
  | <code>Iridium GO  Ecex</code>                                     | <code>Communications Devices and Accessories</code>                                      |
  | <code>LC/LC SM 9/125 DX 1.8mm Lszh L 10m</code>                   | <code>Components for information technology or broadcasting or telecommunications</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `fp16`: True

#### 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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 0.0
- `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`: 0
- `dataloader_prefetch_factor`: None
- `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`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `dispatch_batches`: None
- `split_batches`: 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
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | spearman_max |
|:------:|:----:|:-------------:|:---------------:|:------------:|
| 0.0637 | 100  | 2.2804        | 1.9512          | nan          |
| 0.1274 | 200  | 1.8803        | 1.9189          | nan          |
| 0.1911 | 300  | 1.8687        | 1.7873          | nan          |
| 0.2548 | 400  | 1.7455        | 1.7351          | nan          |
| 0.3185 | 500  | 1.714         | 1.6717          | nan          |
| 0.3822 | 600  | 1.6956        | 1.6789          | nan          |
| 0.4459 | 700  | 1.7134        | 1.6407          | nan          |
| 0.5096 | 800  | 1.7059        | 1.6175          | nan          |
| 0.5732 | 900  | 1.674         | 1.6256          | nan          |
| 0.6369 | 1000 | 1.6725        | 1.5826          | nan          |
| 0.7006 | 1100 | 1.6238        | 1.5815          | nan          |
| 0.7643 | 1200 | 1.5819        | 1.5684          | nan          |
| 0.8280 | 1300 | 1.526         | 1.5511          | nan          |
| 0.8917 | 1400 | 1.4976        | 1.5496          | nan          |
| 0.9554 | 1500 | 1.5709        | 1.5358          | nan          |
| 1.0191 | 1600 | 1.4731        | 1.5498          | nan          |
| 1.0828 | 1700 | 1.3914        | 1.5280          | nan          |
| 1.1465 | 1800 | 1.4137        | 1.4980          | nan          |
| 1.2102 | 1900 | 1.3964        | 1.5012          | nan          |
| 1.2739 | 2000 | 1.4244        | 1.4972          | nan          |
| 1.3376 | 2100 | 1.4567        | 1.4943          | nan          |
| 1.4013 | 2200 | 1.4224        | 1.4880          | nan          |
| 1.4650 | 2300 | 1.4452        | 1.4685          | nan          |
| 1.5287 | 2400 | 1.3843        | 1.4976          | nan          |
| 1.5924 | 2500 | 1.4538        | 1.4715          | nan          |
| 1.6561 | 2600 | 1.3864        | 1.4738          | nan          |
| 1.7197 | 2700 | 1.3514        | 1.4724          | nan          |
| 1.7834 | 2800 | 1.4295        | 1.4538          | nan          |
| 1.8471 | 2900 | 1.3631        | 1.4629          | nan          |
| 1.9108 | 3000 | 1.3654        | 1.4588          | nan          |
| 1.9745 | 3100 | 1.3335        | 1.4552          | nan          |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.19.1

## 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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