---
base_model: BAAI/bge-small-en-v1.5
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Autonomous diagnostic and recovery protocols are embedded within the power
    management system to isolate and rectify faults, ensuring mission continuity.
- text: The satellite thermal control subsystem (TCS) is crucial for maintaining operational
    temperatures of all onboard instruments and systems within their specified limits.
- text: How does the choice of oxidizer, such as liquid oxygen or nitrogen tetroxide,
    affect the performance and handling requirements of a rocket engine?
- text: The energy conversion efficiency of solar cells is influenced by factors such
    as temperature, radiation exposure, and the angle of incidence of sunlight, necessitating
    adaptive control mechanisms.
- text: The thermal control subsystem must accommodate both internal heat generated
    by electronic components and external thermal loads from the space environment.
inference: true
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 1.0
      name: Accuracy
---

# SetFit with BAAI/bge-small-en-v1.5

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Model Details

### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 3 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

### Model Labels
| Label           | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
|:----------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Power Subsystem | <ul><li>'The electrical generation capability of a satellite is primarily determined by the efficiency and surface area of its photovoltaic cells, which convert incident solar radiation into electrical energy.'</li><li>'Energy distribution within the satellite is managed by a network of bus bars and wiring harnesses, designed to minimize resistive losses and maintain voltage stability across all operational conditions.'</li><li>'Redundant power paths and autonomous fault detection mechanisms are implemented to ensure continuous electrical supply even in the event of subsystem failures or external anomalies.'</li></ul> |
| Thermal Control | <ul><li>'Discuss the significance of thermal isolation techniques in preventing heat transfer between satellite components.'</li><li>'The thermal emissivity of radiators and heat pipes is optimized to dissipate the excess heat generated by power electronics, maintaining thermal equilibrium within the satellite.'</li><li>'Deployable radiators can be utilized to increase the heat rejection capacity of a satellite, particularly during high-power operational phases.'</li></ul>                                                                                                                                                     |
| Propulsion      | <ul><li>'The combustion efficiency of a rocket engine depends on factors like propellant mixture ratio, injector design, and combustion chamber pressure.'</li><li>'Liquid rocket engines utilize cryogenic fuels and oxidizers, such as liquid hydrogen and liquid oxygen, which require complex storage and handling systems to maintain their extremely low temperatures.'</li><li>'The nozzle design, including its shape and expansion ratio, significantly influences the exhaust velocity and overall thrust of a rocket engine.'</li></ul>                                                                                                |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 1.0      |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
pip install setfit
```

Then you can load this model and run inference.

```python
from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("patrickfleith/setfit-bge-small-v1.5-sst2-8-shot")
# Run inference
preds = model("How does the choice of oxidizer, such as liquid oxygen or nitrogen tetroxide, affect the performance and handling requirements of a rocket engine?")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 11  | 23.2632 | 30  |

| Label           | Training Sample Count |
|:----------------|:----------------------|
| Propulsion      | 13                    |
| Thermal Control | 13                    |
| Power Subsystem | 12                    |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (10, 10)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0323 | 1    | 0.2123        | -               |
| 1.6129 | 50   | 0.0264        | -               |
| 3.2258 | 100  | 0.0039        | -               |
| 4.8387 | 150  | 0.0034        | -               |
| 6.4516 | 200  | 0.0024        | -               |
| 8.0645 | 250  | 0.0021        | -               |
| 9.6774 | 300  | 0.0021        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.3.1+cu121
- Datasets: 2.20.0
- Tokenizers: 0.15.2

## Citation

### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}
```

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