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---
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base_model: sentence-transformers/paraphrase-mpnet-base-v2
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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: handling washing radiation precaution long leaving scientist tongs source
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effective nature air range row laboratory low experiment keeping sealed temperature
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opening protect
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- text: nucleus proton element charge isotope
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- text: size mean al time sound tuning frequency every marked last second length note
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directly produced fork number travel
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- text: distance circle surface stick frequency water dipped correct vertical longitudinal
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three produced statement two amplitude wave
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- text: rate electromagnetic radiation source correct form stream radioactive penetrating
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detector statement highly
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inference: true
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model-index:
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- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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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: 0.9387755102040817
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name: Accuracy
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---
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# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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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 [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.
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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:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
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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:** 6 classes
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** 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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| 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> |
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| 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> |
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| 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> |
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| 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> |
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| 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> |
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| 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> |
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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** | 0.9388 |
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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("Aurore32/physics-classifier-model")
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# Run inference
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preds = model("nucleus proton element charge isotope")
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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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<!--
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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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-->
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<!--
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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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-->
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<!--
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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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-->
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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 | 2 | 13.375 | 35 |
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| Label | Training Sample Count |
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|:------------|:----------------------|
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| wave | 8 |
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| wave | 8 |
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| magnetism | 8 |
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| wave | 8 |
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| 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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| forces | 8 |
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| nuclear | 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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| thermal | 8 |
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| wave | 8 |
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| nuclear | 8 |
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| electricity | 8 |
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| forces | 8 |
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| wave | 8 |
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| forces | 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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| electricity | 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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| thermal | 8 |
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| electricity | 8 |
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| nuclear | 8 |
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| electricity | 8 |
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| wave | 8 |
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| forces | 8 |
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| nuclear | 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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| forces | 8 |
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| thermal | 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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| forces | 8 |
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| thermal | 8 |
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| thermal | 8 |
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| forces | 8 |
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| forces | 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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| electricity | 8 |
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| wave | 8 |
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| forces | 8 |
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| thermal | 8 |
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| wave | 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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| wave | 8 |
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| wave | 8 |
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| electricity | 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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| electricity | 8 |
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| thermal | 8 |
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| magnetism | 8 |
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| nuclear | 8 |
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| thermal | 8 |
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| wave | 8 |
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| wave | 8 |
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| thermal | 8 |
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| electricity | 8 |
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| electricity | 8 |
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| wave | 8 |
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| forces | 8 |
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| thermal | 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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| 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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| electricity | 8 |
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| electricity | 8 |
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| wave | 8 |
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| thermal | 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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| thermal | 8 |
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| nuclear | 8 |
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| electricity | 8 |
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| magnetism | 8 |
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| nuclear | 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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| nuclear | 8 |
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| thermal | 8 |
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| magnetism | 8 |
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| magnetism | 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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| 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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| thermal | 8 |
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| electricity | 8 |
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| thermal | 8 |
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| electricity | 8 |
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| wave | 8 |
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| wave | 8 |
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| wave | 8 |
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| nuclear | 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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| forces | 8 |
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| forces | 8 |
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| wave | 8 |
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| thermal | 8 |
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| thermal | 8 |
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| thermal | 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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| magnetism | 8 |
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| electricity | 8 |
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| forces | 8 |
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| forces | 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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| thermal | 8 |
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| electricity | 8 |
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| forces | 8 |
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| electricity | 8 |
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| magnetism | 8 |
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| thermal | 8 |
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| forces | 8 |
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| magnetism | 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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| nuclear | 8 |
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| wave | 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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| nuclear | 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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| magnetism | 8 |
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| forces | 8 |
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| wave | 8 |
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| wave | 8 |
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| thermal | 8 |
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| wave | 8 |
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| wave | 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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| forces | 8 |
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| magnetism | 8 |
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| nuclear | 8 |
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| thermal | 8 |
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| magnetism | 8 |
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| electricity | 8 |
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| wave | 8 |
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| magnetism | 8 |
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| thermal | 8 |
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| magnetism | 8 |
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| wave | 8 |
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| forces | 8 |
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| thermal | 8 |
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| thermal | 8 |
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| electricity | 8 |
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| thermal | 8 |
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| forces | 8 |
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| nuclear | 8 |
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| wave | 8 |
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| electricity | 8 |
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| wave | 8 |
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| wave | 8 |
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| magnetism | 8 |
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| electricity | 8 |
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| wave | 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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| magnetism | 8 |
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| thermal | 8 |
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| wave | 8 |
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| thermal | 8 |
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| nuclear | 8 |
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| thermal | 8 |
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| wave | 8 |
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| wave | 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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| electricity | 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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| magnetism | 8 |
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| thermal | 8 |
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| thermal | 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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| thermal | 8 |
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| magnetism | 8 |
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| nuclear | 8 |
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| wave | 8 |
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| wave | 8 |
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| wave | 8 |
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| wave | 8 |
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| wave | 8 |
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| electricity | 8 |
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| forces | 8 |
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| forces | 8 |
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| electricity | 8 |
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| magnetism | 8 |
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| thermal | 8 |
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| magnetism | 8 |
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| thermal | 8 |
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| nuclear | 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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| forces | 8 |
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| wave | 8 |
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| thermal | 8 |
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| wave | 8 |
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| forces | 8 |
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| thermal | 8 |
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| magnetism | 8 |
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| electricity | 8 |
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| magnetism | 8 |
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| wave | 8 |
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| nuclear | 8 |
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| wave | 8 |
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| forces | 8 |
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| thermal | 8 |
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| magnetism | 8 |
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| forces | 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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| thermal | 8 |
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| electricity | 8 |
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| nuclear | 8 |
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| wave | 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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| magnetism | 8 |
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| electricity | 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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| nuclear | 8 |
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| nuclear | 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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| nuclear | 8 |
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| thermal | 8 |
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| forces | 8 |
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| wave | 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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| wave | 8 |
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| magnetism | 8 |
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| nuclear | 8 |
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| wave | 8 |
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| magnetism | 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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| electricity | 8 |
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| electricity | 8 |
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| wave | 8 |
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| wave | 8 |
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| forces | 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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| nuclear | 8 |
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| nuclear | 8 |
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| thermal | 8 |
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| wave | 8 |
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| nuclear | 8 |
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| forces | 8 |
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| nuclear | 8 |
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| thermal | 8 |
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| magnetism | 8 |
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| wave | 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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| wave | 8 |
|
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| wave | 8 |
|
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| electricity | 8 |
|
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| wave | 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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| forces | 8 |
|
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| wave | 8 |
|
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| electricity | 8 |
|
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| wave | 8 |
|
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| forces | 8 |
|
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| thermal | 8 |
|
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| thermal | 8 |
|
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| magnetism | 8 |
|
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| wave | 8 |
|
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| electricity | 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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| thermal | 8 |
|
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| thermal | 8 |
|
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| electricity | 8 |
|
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| nuclear | 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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| electricity | 8 |
|
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| thermal | 8 |
|
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| wave | 8 |
|
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| nuclear | 8 |
|
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| nuclear | 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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| 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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| nuclear | 8 |
|
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| forces | 8 |
|
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| magnetism | 8 |
|
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| magnetism | 8 |
|
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| wave | 8 |
|
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| nuclear | 8 |
|
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| forces | 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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| thermal | 8 |
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| nuclear | 8 |
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| wave | 8 |
|
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| electricity | 8 |
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| wave | 8 |
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| magnetism | 8 |
|
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| electricity | 8 |
|
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| magnetism | 8 |
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| thermal | 8 |
|
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| forces | 8 |
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| forces | 8 |
|
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| thermal | 8 |
|
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| wave | 8 |
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| electricity | 8 |
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| wave | 8 |
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| electricity | 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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| electricity | 8 |
|
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| forces | 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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| nuclear | 8 |
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| forces | 8 |
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| wave | 8 |
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| forces | 8 |
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| magnetism | 8 |
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| thermal | 8 |
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| nuclear | 8 |
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| thermal | 8 |
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| 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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