--- 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 This modelcard aims to be text classification for research abstract regarding to disaster management phase. ## Model Details ### Model Description - **Developed by:** Odirunia - **Shared by [optional]:** [More Information Needed] - **Model type:** Bert - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses ### Direct Use [More Information Needed] ### Downstream Use [optional] [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data [More Information Needed] ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Environmental Impact 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). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]