README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:25012
- loss:MultipleNegativesRankingLoss
base_model: nomic-ai/modernbert-embed-base
widget:
- source_sentence: ÇİFT KLİMALI BARAN FREE COOLING UNIT MONTAJ KITI.
  sentences:
  - Building construction machinery and accessories
  - Building construction machinery and accessories
  - Mounting Hardware
- source_sentence: HUAWEI.TN1-L4G-100GHz-FEC /Line Wavelength Conversion Board with
    4xGigabit Ethernet Line Capacity
  sentences:
  - Fixed network equipment and components
  - Audio and visual equipment
  - System boards processors interfaces or modules
- source_sentence: ASR 9922 System Fan Tray v3, Spare
  sentences:
  - Security and control equipment
  - Computers
  - System boards processors interfaces or modules
- source_sentence: Enhanced Cat.5E UTP Patch Cord 1.5M, White
  sentences:
  - Electrical cable and accessories
  - Computer accessories
  - Air circulation and parts and accessories
- source_sentence: Controller CXC
  sentences:
  - Personal communication devices
  - Fixed network equipment and components
  - Power generation control equipment
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on nomic-ai/modernbert-embed-base
  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
---

# SentenceTransformer based on nomic-ai/modernbert-embed-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base). 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:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision 5960f1566fb7cb1adf1eb6e816639cf4646d9b12 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 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/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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (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-02.12.25-total46clas-ifhavemorethan100sampleperclass-0.71acc")
# Run inference
sentences = [
    'Controller CXC',
    'Power generation control equipment',
    'Personal communication devices',
]
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** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 25,012 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: 4 tokens</li><li>mean: 18.46 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 6.42 tokens</li><li>max: 11 tokens</li></ul> |
* Samples:
  | sentence1                                                                                                     | sentence2                                       |
  |:--------------------------------------------------------------------------------------------------------------|:------------------------------------------------|
  | <code>HPE MSA 14.4T SAS 10K SFF M2 6pk HDD Bdl</code>                                                         | <code>Media storage devices</code>              |
  | <code>Huawei Solar Greensites Solution (Yerli Panel_4*540Wp_Huawei Panel + PVPU+Konstrüksiyon+İşçilik)</code> | <code>Power generation control equipment</code> |
  | <code>NetEngine9000 10G EVPN Port License(per 10G)</code>                                                     | <code>Network management software</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: 3,127 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: 18.0 tokens</li><li>max: 77 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 6.4 tokens</li><li>max: 11 tokens</li></ul> |
* Samples:
  | sentence1                                                      | sentence2                                                                 |
  |:---------------------------------------------------------------|:--------------------------------------------------------------------------|
  | <code>CONNECTION CABLE</code>                                  | <code>Electrical cable and accessories</code>                             |
  | <code>MMU2 B 4-16 (24V, -48V)</code>                           | <code>Electronic component parts and raw materials and accessories</code> |
  | <code>3ft C14 to C13 locking power cable 15A/250V - red</code> | <code>Electrical cable 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"
  }
  ```

### 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`: None
- `hub_always_push`: False
- `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
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:---------------:|
| 0.1279 | 100  | 2.5126        | 2.1189          | nan             |
| 0.2558 | 200  | 1.9979        | 1.9490          | nan             |
| 0.3836 | 300  | 1.8803        | 1.9128          | nan             |
| 0.5115 | 400  | 1.8242        | 1.8253          | nan             |
| 0.6394 | 500  | 1.8024        | 1.7830          | nan             |
| 0.7673 | 600  | 1.7425        | 1.7727          | nan             |
| 0.8951 | 700  | 1.7302        | 1.7469          | nan             |
| 1.0230 | 800  | 1.6722        | 1.7273          | nan             |
| 1.1509 | 900  | 1.4698        | 1.7384          | nan             |
| 1.2788 | 1000 | 1.5151        | 1.7111          | nan             |
| 1.4066 | 1100 | 1.5151        | 1.7173          | nan             |
| 1.5345 | 1200 | 1.494         | 1.6988          | nan             |
| 1.6624 | 1300 | 1.4935        | 1.7058          | nan             |
| 1.7903 | 1400 | 1.5143        | 1.6664          | nan             |
| 1.9182 | 1500 | 1.5253        | 1.6636          | nan             |
| 2.0460 | 1600 | 1.4355        | 1.6781          | nan             |
| 2.1739 | 1700 | 1.3638        | 1.6944          | nan             |
| 2.3018 | 1800 | 1.319         | 1.6829          | nan             |
| 2.4297 | 1900 | 1.2848        | 1.7047          | nan             |
| 2.5575 | 2000 | 1.3207        | 1.6950          | nan             |
| 2.6854 | 2100 | 1.2769        | 1.6911          | nan             |
| 2.8133 | 2200 | 1.2934        | 1.6958          | nan             |
| 2.9412 | 2300 | 1.3244        | 1.6897          | nan             |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0.dev0
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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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