---
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: The thrust chamber is a critical component where the combustion of propellants
    occurs, generating high-pressure and high-temperature exhaust gases.
- text: What are the primary challenges in developing reusable rocket engines, and
    how do they impact cost and reliability?
- text: The integration of maximum power point tracking (MPPT) technology enhances
    the efficiency of solar arrays by dynamically adjusting the load to match the
    optimal power output of the photovoltaic cells.
- text: In liquid rocket engines, the turbopump plays a vital role in feeding propellants
    into the combustion chamber at high pressures.
- text: Discuss the significance of thermal isolation techniques in preventing heat
    transfer between satellite components.
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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
|:----------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Propulsion      | <ul><li>'The use of staged combustion cycles, such as the full-flow staged combustion cycle, can enhance the performance of liquid rocket engines by utilizing propellants more efficiently.'</li><li>"Rocket engines operate on the principle of Newton's Third Law of Motion, where the expulsion of high-speed exhaust gases produces a reaction force that propels the rocket forward."</li><li>'The efficiency of a rocket engine is primarily determined by its specific impulse (Isp), which measures the thrust produced per unit of propellant consumed.'</li></ul>                          |
| Power Subsystem | <ul><li>"The satellite's power budget, which balances generation, storage, and consumption, is meticulously planned to ensure that all systems remain operational throughout the mission duration."</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>'Autonomous diagnostic and recovery protocols are embedded within the power management system to isolate and rectify faults, ensuring mission continuity.'</li></ul> |
| Thermal Control | <ul><li>'Thermo-optical properties of surface materials, such as absorptivity and emissivity, are critical parameters in the design of the thermal control subsystem.'</li><li>'Phase change materials (PCMs) are employed in some satellite TCS designs to absorb and release thermal energy, stabilizing temperature fluctuations during orbital transitions.'</li><li>'Discuss the advantages and limitations of using variable conductance heat pipes (VCHPs) in spacecraft.'</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/my-awesome-astro-text-classifier")
# Run inference
preds = model("Discuss the significance of thermal isolation techniques in preventing heat transfer between satellite components.")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 11  | 22.5278 | 30  |

| Label           | Training Sample Count |
|:----------------|:----------------------|
| Propulsion      | 12                    |
| Thermal Control | 13                    |
| Power Subsystem | 11                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0370 | 1    | 0.2051        | -               |
| 1.8519 | 50   | 0.0194        | -               |
| 3.7037 | 100  | 0.0048        | -               |
| 5.5556 | 150  | 0.0031        | -               |
| 7.4074 | 200  | 0.0025        | -               |
| 9.2593 | 250  | 0.0025        | -               |

### 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->