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--- |
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base_model: BAAI/bge-small-en-v1.5 |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: How does the choice of oxidizer, such as liquid oxygen or nitrogen tetroxide, |
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affect the performance and handling requirements of a rocket engine? |
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- text: Rocket engines designed for vacuum operation often incorporate radiative cooling |
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methods, utilizing large surface areas to dissipate heat in the absence of convective |
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cooling mechanisms. |
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- text: Thermo-optical properties of surface materials, such as absorptivity and emissivity, |
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are critical parameters in the design of the thermal control subsystem. |
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- text: The thrust produced by a rocket engine is a function of the mass flow rate |
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of the propellant and the velocity of the exhaust gases as they exit the nozzle. |
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- text: Thermal analysis of a satellite involves finite element modeling to predict |
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temperature gradients and ensure proper thermal design and component placement. |
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inference: true |
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model-index: |
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- name: SetFit with BAAI/bge-small-en-v1.5 |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 1.0 |
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name: Accuracy |
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--- |
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# SetFit with BAAI/bge-small-en-v1.5 |
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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. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 3 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:----------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| 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> | |
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| 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> | |
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| 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> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 1.0 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("patrickfleith/my-awesome-astro-text-classifier") |
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# Run inference |
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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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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 11 | 22.2368 | 30 | |
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| Label | Training Sample Count | |
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|:----------------|:----------------------| |
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| Propulsion | 15 | |
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| Thermal Control | 14 | |
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| Power Subsystem | 9 | |
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### Training Hyperparameters |
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- batch_size: (32, 32) |
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- num_epochs: (10, 10) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0333 | 1 | 0.2377 | - | |
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| 1.6667 | 50 | 0.0551 | - | |
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| 3.3333 | 100 | 0.0046 | - | |
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| 5.0 | 150 | 0.0031 | - | |
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| 6.6667 | 200 | 0.0024 | - | |
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| 8.3333 | 250 | 0.0022 | - | |
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| 10.0 | 300 | 0.002 | - | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.39.0 |
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- PyTorch: 2.3.1+cu121 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.15.2 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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