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--- |
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license: apache-2.0 |
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datasets: |
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- race |
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language: |
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- en |
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tags: |
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- text classification |
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- multiple-choice |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model was finetuned on RACE for multiple choice (text classification). The initial model used was distilroberta-base https://huggingface.co/distilroberta-base |
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The model was trained using the code from https://github.com/zphang/lrqa. Please refer to and cite the authors. |
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# Model Details |
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- **Initial model:** distilroberta-base |
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- **LR:** 1e-5 |
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- **Epochs:** 3 |
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- **Warmup Ratio:** 0.1 (10%) |
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- **Batch Size:** 16 |
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- **Max Seq Len:** 512 |
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## Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Model type:** [DistilRoBERTa] |
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- **Language(s) (NLP):** [English] |
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- **License:** [Apache-2.0] |
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- **Finetuned from model [optional]:** [distilroberta-base] |
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## Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [https://github.com/zphang/lrqa] |
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- **Dataset:** [https://huggingface.co/datasets/race] |
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# Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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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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[More Information Needed] |
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# Training Details |
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## Training Data |
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<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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[More Information Needed] |
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# Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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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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- **Hardware Type:** A100 - 40GB |
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- **Hours used:** 4 |
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- **Cloud Provider:** Private |
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- **Compute Region:** Portugal |
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- **Carbon Emitted:** 0.18 kgCO2 |
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Experiments were conducted using a private infrastructure, which has a carbon efficiency of 0.178 kgCO$_2$eq/kWh. A cumulative of 4 hours of computation was performed on hardware of type A100 PCIe 40/80GB (TDP of 250W). |
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Total emissions are estimated to be 0.18 kgCO$_2$eq of which 0 percent were directly offset. |
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Estimations were conducted using the \href{https://mlco2.github.io/impact#compute}{MachineLearning Impact calculator} presented in \cite{lacoste2019quantifying}. |
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