setfit safetensors bert sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:BAAI/bge-small-en-v1.5 base_model:finetune:BAAI/bge-small-en-v1.5 model-index region:us
---
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: How does the choice of oxidizer, such as liquid oxygen or nitrogen tetroxide,
    affect the performance and handling requirements of a rocket engine?
- text: Rocket engines designed for vacuum operation often incorporate radiative cooling
    methods, utilizing large surface areas to dissipate heat in the absence of convective
    cooling mechanisms.
- text: Thermo-optical properties of surface materials, such as absorptivity and emissivity,
    are critical parameters in the design of the thermal control subsystem.
- text: The thrust produced by a rocket engine is a function of the mass flow rate
    of the propellant and the velocity of the exhaust gases as they exit the nozzle.
- text: Thermal analysis of a satellite involves finite element modeling to predict
    temperature gradients and ensure proper thermal design and component placement.
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>"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 combustion efficiency of a rocket engine depends on factors like propellant mixture ratio, injector design, and combustion chamber pressure.'</li><li>'Deep throttling capability, which allows a rocket engine to vary its thrust over a wide range, is essential for applications requiring precise landing maneuvers, such as lunar landers.'</li></ul>                          |
| Power Subsystem | <ul><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><li>'Electromagnetic interference (EMI) shielding and grounding techniques are essential in satellite design to prevent power system noise from affecting sensitive communication and navigation subsystems.'</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>'The thermal control subsystem must accommodate both internal heat generated by electronic components and external thermal loads from the space environment.'</li><li>'Describe the impact of albedo and infrared emissions from Earth on satellite thermal design.'</li><li>'Passive thermal control elements, such as multi-layer insulation (MLI), surface coatings, and radiators, are used to minimize thermal fluctuations and radiation absorption.'</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("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  | 22.2368 | 30  |

| Label           | Training Sample Count |
|:----------------|:----------------------|
| Propulsion      | 15                    |
| Thermal Control | 14                    |
| Power Subsystem | 9                     |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0333 | 1    | 0.2377        | -               |
| 1.6667 | 50   | 0.0551        | -               |
| 3.3333 | 100  | 0.0046        | -               |
| 5.0    | 150  | 0.0031        | -               |
| 6.6667 | 200  | 0.0024        | -               |
| 8.3333 | 250  | 0.0022        | -               |
| 10.0   | 300  | 0.002         | -               |

### 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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