metadata

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
            name: Accuracy

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

This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

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

Fine-tuning a Sentence Transformer with contrastive learning.
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
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 3 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
Power Subsystem	'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.' '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.' '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.'
Thermal Control	'Discuss the significance of thermal isolation techniques in preventing heat transfer between satellite components.' '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.' 'Deployable radiators can be utilized to increase the heat rejection capacity of a satellite, particularly during high-power operational phases.'
Propulsion	'The combustion efficiency of a rocket engine depends on factors like propellant mixture ratio, injector design, and combustion chamber pressure.' '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.' 'The nozzle design, including its shape and expansion ratio, significantly influences the exhaust velocity and overall thrust of a rocket engine.'

Evaluation

Metrics

Label	Accuracy
all	1.0

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

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?")

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

@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}
}