Update README.md

README.md

@@ -8,22 +8,20 @@ Using the [DistilRoBERTa](https://huggingface.co/distilroberta-base) model as st
 ### Climate performance model card
 
 
-| Minimum card                                                             |                |
+| distilroberta-base-climate-f                                             |                |
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 | 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        |
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-| Extended card                                                            |                |
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 | 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 |
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