README.md

---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:10K<n<100K
- loss:CosineSimilarityLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
- source_sentence: Tech Writer
  sentences:
  - Tech Specialist
  - Architectural Historian
  - Order Selector (Picker)
- source_sentence: Controller
  sentences:
  - Assistant Controller
  - Key Accounts Supervisor
  - Cosmetologist - Plus Tips
- source_sentence: Accountant
  sentences:
  - Financial Accountant
  - Manager, Corporate Sales
  - Materials Sourcing Lead
- source_sentence: Planner III
  sentences:
  - Strategist
  - Product Finance Manager
  - Materials Sourcing Lead
- source_sentence: AP Analyst
  sentences:
  - AP Specialist
  - ESCO Service Coordinator
  - Boiler Tender Lead
pipeline_tag: sentence-similarity
---

# 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 84f2bcc00d77236f9e89c8a360a00fb1139bf47d -->
- **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("JuanIgnacioSolerno/all-mpnet-base-v2-sts")
# Run inference
sentences = [
    'AP Analyst',
    'AP Specialist',
    'ESCO Service Coordinator',
]
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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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 11,923 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                        | sentence2                                                                      | score                                                          |
  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                           | string                                                                         | float                                                          |
  | details | <ul><li>min: 3 tokens</li><li>mean: 7.17 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 4.0 tokens</li><li>max: 4 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.04</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                    | sentence2                   | score            |
  |:-----------------------------------------------------------------------------|:----------------------------|:-----------------|
  | <code>Land Coordinator, Renewable Development</code>                         | <code>Energy Analyst</code> | <code>0.0</code> |
  | <code>Customer Service Advocate - Remote within the state of Colorado</code> | <code>Energy Analyst</code> | <code>0.0</code> |
  | <code>Global Head of Infrastructure</code>                                   | <code>Energy Analyst</code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 2,981 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                        | sentence2                                                                      | score                                                          |
  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                           | string                                                                         | float                                                          |
  | details | <ul><li>min: 3 tokens</li><li>mean: 7.21 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 4.0 tokens</li><li>max: 4 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.05</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                            | sentence2                   | score            |
  |:---------------------------------------------------------------------|:----------------------------|:-----------------|
  | <code>IT Data Coordinator - Customer Data & Integrations Team</code> | <code>Energy Analyst</code> | <code>0.0</code> |
  | <code>Warehouse Associate</code>                                     | <code>Energy Analyst</code> | <code>0.0</code> |
  | <code>Human Resources Manager</code>                                 | <code>Energy Analyst</code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.0.0.post200
- Accelerate: 0.30.1
- Datasets: 2.19.1
- 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",
}
```

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