Update README.md

README.md

@@ -24,6 +24,16 @@ datasets:
 
 ## Model description
 
+## Training data
+
+## Evaluation results
+
+This model achieves the following results (meassured using the validation portion of the [wikiann](https://huggingface.co/datasets/wikiann)):
+
+| Metric | Value |
+|:------:|:----:|
+|loss | 87.6 |
+
 ## About RoBERTa
 
 This model is a fine-tuned version of [XLM-RoBERTa](https://huggingface.co/xlm-roberta-large). The original model was pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Conneau et al. and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/xlmr). 
@@ -38,10 +48,6 @@ This way, the model learns an inner representation of 100 languages that can the
 
 This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.  
 
-## Training data
-
-## Metrics
-
 ## Usage
 
 You can use this model by using the AutoTokenize and AutoModelForTokenClassification class: