| --- |
| license: afl-3.0 |
| language: |
| - en |
| - id |
| metrics: |
| - name: accuracy |
| value: 0.9394 |
| - name: precision |
| value: 0.9413 |
| - name: recall |
| value: 0.9394 |
| - name: F1-Score |
| value: 0.9388 |
| library_name: transformers |
| tags: |
| - bert |
| - research abstract |
| widget: |
| - text: >- |
| Following the tsunami in December 2004 in Aceh, Indonesia, there has been a |
| massive programme of rebuilding permanent houses for the tsunami victims. |
| The houses are of various designs, and the internal conditions and thermal |
| performance vary considerably. This paper is aimed at assessing comfort in a |
| number of these houses, and is based on measurements from ten designs of |
| post tsunami houses conducted between 22nd May and 4th July 2009. These ten |
| house types are categorized by different form, design and materials, two |
| houses of each type being represented in the results. Air and surface |
| temperatures, relative humidity, and air velocity were measured and |
| questionnaires on thermal comfort were filled in by the occupants. The |
| results show an interesting range of temperature and humidity, ranging from |
| 250C-380C indoors and 210C-41.40C outdoors, relative humidity of 40%-86% |
| indoors, compared with 26%-98% outdoors. The households qualify their house |
| comfort by voting seven thermal sensation scales. |
| example_title: recovery |
| --- |
| # Model Card for Model ID |
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|
| <!-- Provide a quick summary of what the model is/does. --> |
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| This modelcard aims to be text classification for research abstract regarding to disaster management phase. |
| ## Model Details |
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| ### Model Description |
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| <!-- Provide a longer summary of what this model is. --> |
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| - **Developed by:** Odirunia |
| - **Shared by [optional]:** [More Information Needed] |
| - **Model type:** Bert |
| - **Language(s) (NLP):** [More Information Needed] |
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| - **Finetuned from model [optional]:** [More Information Needed] |
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| ### Model Sources [optional] |
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| - **Repository:** [More Information Needed] |
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| ## Uses |
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| ### Direct Use |
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| ### Downstream Use [optional] |
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| ### Out-of-Scope Use |
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| ## Bias, Risks, and Limitations |
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| ### Recommendations |
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| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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| ## How to Get Started with the Model |
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| Use the code below to get started with the model. |
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| ## Training Details |
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| ### Training Data |
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| ### Training Procedure |
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| #### Preprocessing [optional] |
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| #### Training Hyperparameters |
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| - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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| ## Evaluation |
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| #### Factors |
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| #### Metrics |
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| ### Results |
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| #### Summary |
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| ## Model Examination [optional] |
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| ## Environmental Impact |
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| <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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| Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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| - **Hardware Type:** [More Information Needed] |
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| ## Technical Specifications [optional] |
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| ### Model Architecture and Objective |
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| ## Citation [optional] |
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| **BibTeX:** |
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| **APA:** |
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| ## Glossary [optional] |
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| ## More Information [optional] |
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| ## Model Card Authors [optional] |
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| ## Model Card Contact |
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