Update README.md

README.md

@@ -13,8 +13,9 @@ metrics:
 
 A multilingual web register classifier, fine-tuned from XLM-RoBERTa-large. 
 The model is trained with the multilingual CORE corpora across five languages (English, Finnish, French, Swedish, Turkish) to classify documents based on the CORE taxonomy, detailed below. 
-The model demonstrates state-of-the-art performance in classifying web registers and achieves good zero-shot performance for additional languages.
+The model demonstrates state-of-the-art performance in classifying web registers and achieves good zero-shot performance for additional languages (see Evaluation below).
 It is designed to support the development of open language models and for linguists analyzing register variation.
+
 ## Model Details
 
 ### Model Description
@@ -69,7 +70,7 @@ The main labels are uppercase. To only include these main labels in the predicti
 
 Use the code below to get started with the model.
 
-```
+```python
 import torch
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
@@ -133,7 +134,7 @@ Average inference time (across 1000 iterations), using a single NVIDIA A100 GPU
 
 | Language | F1 (All labels) | F1 (Main labels) |
 | -------- | --------------- | ---------------- |
-| English  | 0.72            | 
+| English  | 0.72            | 0.75
 | Finnish  | 0.79            | 
 | French   | 0.75            | 
 | Swedish  | 0.81            |