Update README.md

README.md

@@ -3,9 +3,34 @@ language: en
 license: apache-2.0
 ---
 
+# Model Card for distilroberta-base-climate-s
+
+## Model Description
+
+This is the ClimateBERT language model based on the SIM-SELECT sample selection strategy.
+
+*Note: We generally recommend choosing the [distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model over this language model (unless you have good reasons not to).*
+
 Using the [DistilRoBERTa](https://huggingface.co/distilroberta-base) model as starting point, the ClimateBERT Language Model is additionally pretrained on a text corpus comprising climate-related research paper abstracts, corporate and general news and reports from companies. The underlying methodology can be found in our [language model research paper](https://arxiv.org/abs/2110.12010).
 
-### BibTeX entry and citation info
+## Climate performance card
+
+| distilroberta-base-climate-s                                             |                |
+|--------------------------------------------------------------------------|----------------|
+| 1. Is the resulting model publicly available?                            | Yes            |
+| 2. How much time does the training of the final model take?              | 8 hours        |
+| 3. How much time did all experiments take (incl. hyperparameter search)? | 288 hours      |
+| 4. What was the energy consumption (GPU/CPU)?                            | 0.7 kW         |
+| 5. At which geo location were the computations performed?                | Germany        |
+| 6. What was the energy mix at the geo location?                          | 470 gCO2eq/kWh |
+| 7. How much CO2eq was emitted to train the final model?                  | 2.63 kg        |
+| 8. How much CO2eq was emitted for all experiments?                       | 94.75 kg       |
+| 9. What is the average CO2eq emission for the inference of one sample?   | 0.62 mg        |
+| 10. Which positive environmental impact can be expected from this work?  | This work can be categorized as a building block tools following Jin et al (2021). It supports the training of NLP models in the field of climate change and, thereby, have a positive environmental impact in the future. |
+| 11. Comments                                                             | Block pruning could decrease CO2eq emissions |
+
+### Citation Information
+
 ```bibtex
 @article{wkbl2021,
         title={ClimateBERT: A Pretrained Language Model for Climate-Related Text},