| ---
|
| base_model: sentence-transformers/paraphrase-mpnet-base-v2
|
| library_name: setfit
|
| metrics:
|
| - accuracy
|
| pipeline_tag: text-classification
|
| tags:
|
| - setfit
|
| - sentence-transformers
|
| - text-classification
|
| - generated_from_setfit_trainer
|
| widget:
|
| - text: handling washing radiation precaution long leaving scientist tongs source
|
| effective nature air range row laboratory low experiment keeping sealed temperature
|
| opening protect
|
| - text: nucleus proton element charge isotope
|
| - text: size mean al time sound tuning frequency every marked last second length note
|
| directly produced fork number travel
|
| - text: distance circle surface stick frequency water dipped correct vertical longitudinal
|
| three produced statement two amplitude wave
|
| - text: rate electromagnetic radiation source correct form stream radioactive penetrating
|
| detector statement highly
|
| inference: true
|
| model-index:
|
| - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
|
| results:
|
| - task:
|
| type: text-classification
|
| name: Text Classification
|
| dataset:
|
| name: Unknown
|
| type: unknown
|
| split: test
|
| metrics:
|
| - type: accuracy
|
| value: 0.9387755102040817
|
| name: Accuracy
|
| ---
|
|
|
| # SetFit with sentence-transformers/paraphrase-mpnet-base-v2
|
|
|
| This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) 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:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
|
| - **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:** 6 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 |
|
| |:------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| | wave | <ul><li>'sound infrared wave ultraviolet transverse list diagram light'</li><li>'size mean al time sound tuning frequency every marked last second length note directly produced fork number travel'</li><li>'nearby series sound higher frequency air rarefaction correctly pressure amplitude compression row certain lower wave'</li></ul> |
|
| | forces | <ul><li>'extension diagram spring free load three applied handle'</li><li>'different extension spring elastic student proportional length directly object weight diagram cord mass much hung'</li><li>'distance plank long ground load force beam mass value'</li></ul> |
|
| | electricity | <ul><li>'two diagram circuit parallel connected'</li><li>'lamp resistance student various thermistor current circuit parallel happen'</li><li>'resistance student two shown reading ammeter resistor identical circuit voltmeter determine'</li></ul> |
|
| | magnetism | <ul><li>'bar suitable two across made magnet metal electromagnet diagram permanent core'</li><li>'magnetic student hard magnet rod iron material magnetism correct use statement steel permanent soft make'</li><li>'magnetic bar student two hard magnet rod iron material close nickel use steel permanent soft make'</li></ul> |
|
| | nuclear | <ul><li>'nucleus proton element charge isotope'</li><li>'different five'</li><li>'rate electromagnetic radiation source correct form stream radioactive penetrating detector statement highly'</li></ul> |
|
| | thermal | <ul><li>'cooling air top unit stay density transfer correct near thermal move diagram statement refrigerator energy movement'</li><li>'dense position air cold warmer heater flask two room fit less hot possible fitted diagram warm throughout um liquid better'</li><li>'power balance copper student thermometer heat heater substance watch calculate need specific initially solid mass apparatus block capacity electrical key'</li></ul> |
|
|
|
| ## Evaluation
|
|
|
| ### Metrics
|
| | Label | Accuracy |
|
| |:--------|:---------|
|
| | **all** | 0.9388 |
|
|
|
| ## 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("Aurore32/physics-classifier-model")
|
| # Run inference
|
| preds = model("nucleus proton element charge isotope")
|
| ```
|
|
|
| <!--
|
| ### 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 | 2 | 13.375 | 35 |
|
|
|
| | Label | Training Sample Count |
|
| |:------------|:----------------------|
|
| | wave | 8 |
|
| | wave | 8 |
|
| | magnetism | 8 |
|
| | wave | 8 |
|
| | magnetism | 8 |
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| | thermal | 8 |
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| | thermal | 8 |
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| | thermal | 8 |
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| | nuclear | 8 |
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| | thermal | 8 |
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| | nuclear | 8 |
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| | electricity | 8 |
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| | nuclear | 8 |
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| | forces | 8 |
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| | wave | 8 |
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| | nuclear | 8 |
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| | forces | 8 |
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| | thermal | 8 |
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| | wave | 8 |
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| | nuclear | 8 |
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| | thermal | 8 |
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| | nuclear | 8 |
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| | thermal | 8 |
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| | thermal | 8 |
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| | thermal | 8 |
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| | electricity | 8 |
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| | electricity | 8 |
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| | thermal | 8 |
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| | forces | 8 |
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| | electricity | 8 |
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| | nuclear | 8 |
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| | thermal | 8 |
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| | magnetism | 8 |
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| | thermal | 8 |
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| | thermal | 8 |
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| | forces | 8 |
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| | wave | 8 |
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| | nuclear | 8 |
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| | nuclear | 8 |
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| | wave | 8 |
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| | thermal | 8 |
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| | magnetism | 8 |
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| | forces | 8 |
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| | magnetism | 8 |
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| | electricity | 8 |
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| | nuclear | 8 |
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| | magnetism | 8 |
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| | wave | 8 |
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| | thermal | 8 |
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| | wave | 8 |
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| | thermal | 8 |
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| | thermal | 8 |
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| | wave | 8 |
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| | magnetism | 8 |
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| | wave | 8 |
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| | nuclear | 8 |
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| | nuclear | 8 |
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| | magnetism | 8 |
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| | electricity | 8 |
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| | wave | 8 |
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| | nuclear | 8 |
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| | electricity | 8 |
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| | magnetism | 8 |
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| | magnetism | 8 |
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| | forces | 8 |
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| | electricity | 8 |
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| | wave | 8 |
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| | nuclear | 8 |
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| | nuclear | 8 |
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| | nuclear | 8 |
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| | electricity | 8 |
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| | thermal | 8 |
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| | wave | 8 |
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| | thermal | 8 |
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| | thermal | 8 |
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| | nuclear | 8 |
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| | wave | 8 |
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| | wave | 8 |
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| | forces | 8 |
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| | wave | 8 |
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| | electricity | 8 |
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| | nuclear | 8 |
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| | wave | 8 |
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| | magnetism | 8 |
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| | wave | 8 |
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| | thermal | 8 |
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| | electricity | 8 |
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| | thermal | 8 |
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| | thermal | 8 |
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| | wave | 8 |
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| | magnetism | 8 |
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| | thermal | 8 |
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| | wave | 8 |
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| | magnetism | 8 |
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| | wave | 8 |
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| | forces | 8 |
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| | nuclear | 8 |
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| | thermal | 8 |
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| | thermal | 8 |
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| | magnetism | 8 |
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| | electricity | 8 |
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| | nuclear | 8 |
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| | magnetism | 8 |
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| | magnetism | 8 |
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| | wave | 8 |
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| | electricity | 8 |
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| | forces | 8 |
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| | magnetism | 8 |
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| | wave | 8 |
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| | magnetism | 8 |
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| | electricity | 8 |
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| | thermal | 8 |
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| | magnetism | 8 |
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| | forces | 8 |
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| | magnetism | 8 |
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| | magnetism | 8 |
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| | magnetism | 8 |
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| | thermal | 8 |
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| | electricity | 8 |
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| | thermal | 8 |
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| | nuclear | 8 |
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| | wave | 8 |
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| | thermal | 8 |
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| | nuclear | 8 |
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| | magnetism | 8 |
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| | wave | 8 |
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| | thermal | 8 |
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| | electricity | 8 |
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| | thermal | 8 |
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| | thermal | 8 |
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| | magnetism | 8 |
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| | thermal | 8 |
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| | magnetism | 8 |
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| | electricity | 8 |
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| | nuclear | 8 |
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| | wave | 8 |
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| | thermal | 8 |
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| | electricity | 8 |
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| | thermal | 8 |
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| | electricity | 8 |
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| | electricity | 8 |
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| | magnetism | 8 |
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| | wave | 8 |
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| | thermal | 8 |
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| | nuclear | 8 |
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| | nuclear | 8 |
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| | magnetism | 8 |
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| | thermal | 8 |
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| | electricity | 8 |
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| | electricity | 8 |
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| | electricity | 8 |
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| | wave | 8 |
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| | nuclear | 8 |
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| | wave | 8 |
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| | wave | 8 |
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| | magnetism | 8 |
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| | thermal | 8 |
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| | electricity | 8 |
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| | magnetism | 8 |
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| | electricity | 8 |
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| | magnetism | 8 |
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| | nuclear | 8 |
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| | magnetism | 8 |
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| | magnetism | 8 |
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| | thermal | 8 |
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| | wave | 8 |
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| | magnetism | 8 |
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| | nuclear | 8 |
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| | electricity | 8 |
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| | electricity | 8 |
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| | electricity | 8 |
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| | magnetism | 8 |
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| | electricity | 8 |
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| | electricity | 8 |
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| | forces | 8 |
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| | forces | 8 |
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| | forces | 8 |
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| | wave | 8 |
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|
| | nuclear | 8 |
|
| | wave | 8 |
|
| | nuclear | 8 |
|
| | wave | 8 |
|
| | wave | 8 |
|
| | magnetism | 8 |
|
| | nuclear | 8 |
|
| | forces | 8 |
|
| | magnetism | 8 |
|
| | magnetism | 8 |
|
| | wave | 8 |
|
| | nuclear | 8 |
|
| | forces | 8 |
|
| | magnetism | 8 |
|
| | thermal | 8 |
|
| | wave | 8 |
|
| | magnetism | 8 |
|
| | thermal | 8 |
|
| | nuclear | 8 |
|
| | wave | 8 |
|
| | electricity | 8 |
|
| | wave | 8 |
|
| | magnetism | 8 |
|
| | electricity | 8 |
|
| | magnetism | 8 |
|
| | thermal | 8 |
|
| | forces | 8 |
|
| | forces | 8 |
|
| | thermal | 8 |
|
| | wave | 8 |
|
| | electricity | 8 |
|
| | wave | 8 |
|
| | electricity | 8 |
|
| | wave | 8 |
|
| | thermal | 8 |
|
| | thermal | 8 |
|
| | nuclear | 8 |
|
| | electricity | 8 |
|
| | forces | 8 |
|
| | nuclear | 8 |
|
| | magnetism | 8 |
|
| | magnetism | 8 |
|
| | wave | 8 |
|
| | nuclear | 8 |
|
| | forces | 8 |
|
| | wave | 8 |
|
| | forces | 8 |
|
| | magnetism | 8 |
|
| | thermal | 8 |
|
| | nuclear | 8 |
|
| | thermal | 8 |
|
| | electricity | 8 |
|
|
|
| ### Training Hyperparameters
|
| - batch_size: (16, 2)
|
| - num_epochs: (1, 16)
|
| - 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
|
| - l2_weight: 0.01
|
| - seed: 42
|
| - eval_max_steps: -1
|
| - load_best_model_at_end: False
|
|
|
| ### Training Results
|
| | Epoch | Step | Training Loss | Validation Loss |
|
| |:------:|:----:|:-------------:|:---------------:|
|
| | 0.0083 | 1 | 0.1733 | - |
|
| | 0.4167 | 50 | 0.075 | - |
|
| | 0.8333 | 100 | 0.0056 | - |
|
|
|
| ### Framework Versions
|
| - Python: 3.12.4
|
| - SetFit: 1.1.1
|
| - Sentence Transformers: 3.4.0
|
| - Transformers: 4.44.2
|
| - PyTorch: 2.5.1+cpu
|
| - Datasets: 3.2.0
|
| - Tokenizers: 0.19.1
|
|
|
| ## 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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|
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