Update README.md

README.md

@@ -26,7 +26,7 @@ It works for all languages the xlm-roberta model is pretrained on ([overview](ht
 ```python
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-model = AutoModelForSequenceClassification.from_pretrained("manifesto-project/manifestoberta-xlm-roberta-56policy-topics-sentence-2023-1")
+model = AutoModelForSequenceClassification.from_pretrained("manifesto-project/manifestoberta-xlm-roberta-56policy-topics-sentence-2023-1-1")
 tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
 
 sentence = "We will restore funding to the Global Environment Facility and the Intergovernmental Panel on Climate Change, to support critical climate science research around the world"
@@ -60,5 +60,5 @@ The model was evaluated on a test set of 199,046 annotated manifesto statements.
 
 |                                                                                                       | Accuracy | Top2_Acc | Top3_Acc | Precision| Recall | F1_Macro | MCC | Cross-Entropy |
 |-------------------------------------------------------------------------------------------------------|:--------:|:--------:|:--------:|:--------:|:------:|:--------:|:---:|:-------------:|
-[Sentence Model](https://huggingface.co/manifesto-project/xlm-roberta-political-56topics-sentence-2023a)|   0.57   |   0.73   |	  0.81   |	  0.49  |  0.43  |	 0.45   | 0.55|	     1.5      |
-[Context Model](https://huggingface.co/manifesto-project/xlm-roberta-political-56topics-context-2023a)  |   0.64   |   0.81   |   0.88   |    0.54  |  0.52  |   0.53   | 0.62|      1.15     |
+[Sentence Model](https://huggingface.co/manifesto-project/manifestoberta-xlm-roberta-56policy-topics-sentence-2023-1-1)|   0.57   |   0.73   |	  0.81   |	  0.49  |  0.43  |	 0.45   | 0.55|	     1.5      |
+[Context Model](https://huggingface.co/manifesto-project/manifestoberta-xlm-roberta-56policy-topics-context-2023-1-1)  |   0.64   |   0.81   |   0.88   |    0.54  |  0.52  |   0.53   | 0.62|      1.15     |