Update README.md

README.md

@@ -11,20 +11,20 @@ This is the ClimateBERT language model based on the SIM-SELECT sample selection
 
 *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).
+Using the [DistilRoBERTa](https://huggingface.co/distilroberta-base) model as starting point, the ClimateBERT Language Model is additionally pre-trained 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).
 
 ## 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      |
+| 2. How much time does the training of the final model take?              | 48 hours        |
+| 3. How much time did all experiments take (incl. hyperparameter search)? | 350 hours      |
 | 4. What was the power of GPU and 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       |
+| 7. How much CO2eq was emitted to train the final model?                  | 15.79 kg        |
+| 8. How much CO2eq was emitted for all experiments?                       | 115.15 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 |