---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1084
- loss:ContrastiveTensionLossInBatchNegatives
base_model: sentence-transformers/all-mpnet-base-v2
widget:
- source_sentence: Heat and smoke detectors should trigger an alarm and extinguishing
systems.
sentences:
- Laboratory Operators must be trained to manipulate energetic materials correctly.
- Loss or damage to test environments.
- Heat and smoke detectors should trigger an alarm and extinguishing systems.
- source_sentence: Sensitive information regarding the aircrafts, vehicles, payloads
and ground support systems designs or procedures; personnel data; production process;
material; organic or operational safety information, among others, shall be kept
with the allocated restricted access.
sentences:
- Take off / landing executed only when runway is empty.
- Sensitive information regarding the aircrafts, vehicles, payloads and ground support
systems designs or procedures; personnel data; production process; material; organic
or operational safety information, among others, shall be kept with the allocated
restricted access.
- Pilots must not execute maneuver with incorrect climb rate, final altitude, etc.
- source_sentence: Doors close while person in the doorway.
sentences:
- ACS must not provide attitude maneuver commands too late after ASTRO-H has rotated
too far.
- A/C must maintain minimum safe altitude limits.
- Doors close while person in the doorway.
- source_sentence: Doors shall remain closed when train is moving.
sentences:
- Doors shall remain closed when train is moving.
- Catching fire inside the ship.
- Aircraft enters uncontrolled state.
- source_sentence: Customer's data released to public.
sentences:
- Exposure of Earth life or human assets off Earth to toxic, radioactive, or energetic
elements of mission hardware.
- Customer's data released to public.
- Equipment Operated Beyond Limits.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# 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)
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
### 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 š¤ Hub
model = SentenceTransformer("andreyunic23/beds_step4")
# Run inference
sentences = [
"Customer's data released to public.",
"Customer's data released to public.",
'Equipment Operated Beyond Limits.',
]
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]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 1,084 training samples
* Columns: sentence1
, sentence2
, and label
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------|
| type | string | string | int |
| details |
A collision between the ACROBOTER robotic platform and an unknown object must be avoided at all times.
| A collision between the ACROBOTER robotic platform and an unknown object must be avoided at all times.
| 0
|
| A nonāpatient is injured or killed by radiation.
| A nonāpatient is injured or killed by radiation.
| 0
|
| A nonpatient is injured or killed in the process of MRI simulation.
| A nonpatient is injured or killed in the process of MRI simulation.
| 0
|
* Loss: [ContrastiveTensionLossInBatchNegatives
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastivetensionlossinbatchnegatives)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 12
#### All Hyperparameters