|
--- |
|
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?") |
|
``` |
|
|
|
<!-- |
|
### 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 | 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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## 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.* |
|
--> |
