sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:24341 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-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:24341
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: SEPERASYON 3X2
  sentences:
  - Components for information technology or broadcasting or telecommunications
  - Electronic hardware and component parts and accessories
  - Data Voice or Multimedia Network Equipment or Platforms and Accessories
- source_sentence: Cisco 2PK ASR 9K Route Switch Processor Service Edge 880G/slot
  sentences:
  - Data Voice or Multimedia Network Equipment or Platforms and Accessories
  - Data Voice or Multimedia Network Equipment or Platforms and Accessories
  - Communications Devices and Accessories
- source_sentence: CORE NETWORK DONANIM HIZMET 2G3G_HUAWEI
  sentences:
  - Components for information technology or broadcasting or telecommunications
  - Communications Devices and Accessories
  - Components for information technology or broadcasting or telecommunications
- source_sentence: 2,4m GD Satcom Anten Feed Support Kit
  sentences:
  - Communications Devices and Accessories
  - Components for information technology or broadcasting or telecommunications
  - Components for information technology or broadcasting or telecommunications
- source_sentence: TARGUS 13.3  ATMOSPHERE LAPTOP CASE (TNT009EU)
  sentences:
  - Office machines and their supplies and accessories
  - Components for information technology or broadcasting or telecommunications
  - Electrical equipment and components and supplies
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-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/paraphrase-multilingual-MiniLM-L12-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision ae06c001a2546bef168b9bf8f570ccb1a16aaa27 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 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': 128, 'do_lower_case': False}) with Transformer model: 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})
)
```

## 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-18.10.24-ifhavemorethan100sampleperfamily-0.60acc")
# Run inference
sentences = [
    'TARGUS 13.3  ATMOSPHERE LAPTOP CASE (TNT009EU)',
    'Office machines and their supplies and accessories',
    'Electrical equipment and components and supplies',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

<!--
### 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

#### 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** |

<!--
## 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: 24,341 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: 17.37 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.51 tokens</li><li>max: 16 tokens</li></ul> |
* Samples:
  | sentence1                                                                                            | sentence2                                                                                |
  |:-----------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|
  | <code>CISCO.1000BASE-T SFP (NEBS 3 ESD)</code>                                                       | <code>Components for information technology or broadcasting or telecommunications</code> |
  | <code>MINI-LINK 6365 15/A11H</code>                                                                  | <code>Components for information technology or broadcasting or telecommunications</code> |
  | <code>Aruba AP-367 (RW) 802.11n/ac Dual 2x2 2 Radio Integrated Directional Antenna Outdoor AP</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: 3,043 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: 17.51 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.69 tokens</li><li>max: 16 tokens</li></ul> |
* Samples:
  | sentence1                            | sentence2                                                                                |
  |:-------------------------------------|:-----------------------------------------------------------------------------------------|
  | <code>Multicast Analyzer Card</code> | <code>Components for information technology or broadcasting or telecommunications</code> |
  | <code>12m 130x5 MONOPOL Kule</code>  | <code>Structural components and basic shapes</code>                                      |
  | <code>ANT3 A 0.6 23 HPX</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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 3
- `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 |
|:------:|:----:|:-------------:|:---------------:|:------------:|
| 0.1314 | 100  | 3.202         | 2.6937          | nan          |
| 0.2628 | 200  | 2.675         | 2.5088          | nan          |
| 0.3942 | 300  | 2.5367        | 2.4273          | nan          |
| 0.5256 | 400  | 2.4877        | 2.3843          | nan          |
| 0.6570 | 500  | 2.4297        | 2.3481          | nan          |
| 0.7884 | 600  | 2.3945        | 2.3065          | nan          |
| 0.9198 | 700  | 2.343         | 2.2810          | nan          |
| 1.0512 | 800  | 2.2264        | 2.2955          | nan          |
| 1.1827 | 900  | 2.2133        | 2.2620          | nan          |
| 1.3141 | 1000 | 2.2009        | 2.2376          | nan          |
| 1.4455 | 1100 | 2.2104        | 2.2506          | nan          |
| 1.5769 | 1200 | 2.1665        | 2.2462          | nan          |
| 1.7083 | 1300 | 2.1891        | 2.2210          | nan          |
| 1.8397 | 1400 | 2.1694        | 2.2007          | nan          |
| 1.9711 | 1500 | 2.15          | 2.2014          | nan          |
| 2.1025 | 1600 | 2.0314        | 2.2281          | nan          |
| 2.2339 | 1700 | 2.0491        | 2.2212          | nan          |
| 2.3653 | 1800 | 2.015         | 2.2237          | nan          |
| 2.4967 | 1900 | 2.0278        | 2.2185          | nan          |
| 2.6281 | 2000 | 2.0163        | 2.2122          | nan          |
| 2.7595 | 2100 | 1.9732        | 2.2137          | nan          |
| 2.8909 | 2200 | 2.0244        | 2.2096          | 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}
}
```

<!--
## 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.*
-->