---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:723
- loss:ContrastiveTensionLossInBatchNegatives
widget:
- source_sentence: During a rejected takeoff the aircraft departs runway.
  sentences:
  - The ship is approaching shallow water.
  - During a rejected takeoff the aircraft departs runway.
  - A/C must maintain minimum safe altitude limits.
- source_sentence: ACS must provide attitude maneuver commands when ASTRO-H is rotating.
  sentences:
  - Laboratories that handle energetic materials must have laboratory environmental
    control equipment.
  - Loss of functioning democratic society (e.g. loss of freedom, human right .etc.).
  - ACS must provide attitude maneuver commands when ASTRO-H is rotating.
- source_sentence: Overpressurization of plant equipment.
  sentences:
  - Low fuel level after missed approaches.
  - Overpressurization of plant equipment.
  - Aircraft comes too close to service equipment components during operations on
    the ground.
- source_sentence: All the safety/mission critical military aerospace designs and
    products shall be Certified to allow use or operation.
  sentences:
  - Brake light command must illuminate early within X-seconds before stopping vehicle.
  - All the safety/mission critical military aerospace designs and products shall
    be Certified to allow use or operation.
  - ASTRO-H unable to collect scientific data.
- source_sentence: Laboratory equipment out of calibration standards.
  sentences:
  - Certification Authority personal, including Organization Designation Authorization
    (ODA), unqualified to the product in analyse or at the certification process.
  - Staff suffers injury (radiological or physical).
  - Laboratory equipment out of calibration standards.
---

# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision ea78891063587eb050ed4166b20062eaf978037c -->
- **Maximum Sequence Length:** 256 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': 256, '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})
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Laboratory equipment out of calibration standards.',
    'Laboratory equipment out of calibration standards.',
    'Staff suffers injury (radiological or physical).',
]
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]
```

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 723 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 723 samples:
  |         | sentence1                                                                         | sentence2                                                                         | label                        |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------|
  | type    | string                                                                            | string                                                                            | int                          |
  | details | <ul><li>min: 4 tokens</li><li>mean: 13.84 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.84 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
* Samples:
  | sentence1                                                                                              | sentence2                                                                                              | label          |
  |:-------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:---------------|
  | <code>Non-patient injured or killed due to radiation.</code>                                           | <code>Non-patient injured or killed due to radiation.</code>                                           | <code>0</code> |
  | <code>Loss of human life / damage to health and wellbeing (e.g. long term concerns with COVID).</code> | <code>Loss of human life / damage to health and wellbeing (e.g. long term concerns with COVID).</code> | <code>0</code> |
  | <code>The aircraft have insufficient power available.</code>                                           | <code>The aircraft have insufficient power available.</code>                                           | <code>0</code> |
* Loss: [<code>ContrastiveTensionLossInBatchNegatives</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastivetensionlossinbatchnegatives)

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.1
- Transformers: 4.45.2
- PyTorch: 2.5.0+cu121
- Accelerate: 1.0.1
- Datasets: 3.0.2
- Tokenizers: 0.20.1

## 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",
}
```

#### ContrastiveTensionLossInBatchNegatives
```bibtex
@inproceedings{carlsson2021semantic,
    title={Semantic Re-tuning with Contrastive Tension},
    author={Fredrik Carlsson and Amaru Cuba Gyllensten and Evangelia Gogoulou and Erik Ylip{"a}{"a} Hellqvist and Magnus Sahlgren},
    booktitle={International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=Ov_sMNau-PF}
}
```

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