Update README.md

README.md

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 ---
 license: apache-2.0
+datasets:
+- race
+language:
+- en
+tags:
+- question-answering
+- multiple-choice
 ---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model was finetuned on RACE for multiple choice QA. The initial model used was distilroberta-base https://huggingface.co/distilroberta-base
+
+The model was trained using the code from https://github.com/zphang/lrqa. Please refer to and cite the authors.
+
+# Model Details
+
+- **Initial model:** distilroberta-base
+- **LR:** 1e-5
+- **Epochs:** 3
+- **Warmup Ratio:** 0.1 (10%)
+- **Batch Size:** 16
+- **Max Seq Len:** 512
+
+## Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+- **Model type:** [Distil RoBERTa]
+- **Language(s) (NLP):** [English]
+- **License:** [Apache-2.0]
+- **Finetuned from model [optional]:** [distilroberta-base]
+
+## Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [https://github.com/zphang/lrqa]
+- **Dataset:** [https://huggingface.co/datasets/race]
+
+# Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+# Training Details
+
+## Training Data
+
+<!-- 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. -->
+
+[More Information Needed]
+
+
+# Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+# Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+- **Hardware Type:** A100 - 40GB
+- **Hours used:** 4
+- **Cloud Provider:** Private
+- **Compute Region:** Portugal
+- **Carbon Emitted:** 0.18 kgCO2
+
+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).
+Total emissions are estimated to be 0.18 kgCO$_2$eq of which 0 percent were directly offset.
+Estimations were conducted using the \href{https://mlco2.github.io/impact#compute}{MachineLearning Impact calculator} presented in \cite{lacoste2019quantifying}.
+
+
+