Update ReadMe

README.md

@@ -6,7 +6,6 @@ tags:
   - Foundation Model
   - NASA
   - IBM
-library_name: terratorch
 ---
 
 # Prithvi-EO-2.0
@@ -32,7 +31,7 @@ Second, we considered geolocation (center latitude and longitude) and date of ac
 |Prithvi-EO-2.0-600M   | Pretrained 600M parameter model  | [https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-600M](https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-600M) |                                          |
 |Prithvi-EO-2.0-600M-TL   | Pretrained 600M parameter model with temporal and location embeddings | [https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-600M-TL](https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-600M-TL) |
 
-The models were pre-trained at the Julich Supercomputing Center with NASA's HLS V2 product (30m granularity) using 4.2M samples with six bands in the following order: Blue, Green, Red, Narrow NIR, SWIR, SWIR 2.
+The models were pre-trained at the Jülich Supercomputing Centre with NASA's HLS V2 product (30m granularity) using 4.2M samples with six bands in the following order: Blue, Green, Red, Narrow NIR, SWIR, SWIR 2.
 
 ## Benchmarking
 We validated the Prithvi-EO-2.0 models through extensive experiments using [GEO-bench](https://github.com/ServiceNow/geo-bench). Prithvi-EO-2.0-600M-TL outperforms the previous Prithvi-EO model by 8% across a range of tasks. It also outperforms six other geospatial foundation models when benchmarked on remote sensing tasks from different domains and resolutions (i.e. from 0.1m to 15m).