Update README.md

README.md

@@ -248,13 +248,22 @@ The process of creating a diverse, bias-proof, and ethically fair dataset requir
 
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 
-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]
+Experiments were conducted using a private infrastructure, which has a carbon efficiency of 0.432 kgCO$_2$eq/kWh. A cumulative of 1500 hours of computation was performed on hardware of type Tesla V100-SXM2-32GB (TDP of 300W).
+
+Total emissions are estimated to be 194.4 kgCO$_2$eq of which 0 percents were directly offset.
+    
+Estimations were conducted using the [MachineLearning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Quantifying the Carbon Emissions of Machine Learning](https://arxiv.org/abs/1910.09700).
+
+Experiments were conducted using a private infrastructure, which has a carbon efficiency of 0.432 kgCO$_2$eq/kWh. A cumulative of 1500 hours of computation was performed on hardware of type Tesla V100-SXM2-32GB (TDP of 300W).
+
+Total emissions are estimated to be 194.4 kgCO$_2$eq of which 0 percents were directly offset.
+    
+
+- **Hardware Type:** Nvidia V100
+- **Hours used:** 1500
+- **Compute Region:** Bulgaria
+- **Carbon Emitted:** 194.4 kgCO$_2$eq
 
 ## Technical Specifications [optional]