Update ModelCard

README.md

@@ -6,11 +6,11 @@ tags:
 model-index:
 - name: xlm-roberta-base-finetuned-panx-de
   results: []
+language:
+- de
+library_name: transformers
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # xlm-roberta-base-finetuned-panx-de
 
 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
@@ -20,15 +20,23 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model is a fine-tuned version of xlm-roberta-base on the German subset of the PAN-X dataset for Named Entity Recognition (NER). The model has been fine-tuned to perform token classification tasks and is evaluated on its performance in identifying named entities in German text.
 
 ## Intended uses & limitations
 
-More information needed
+### Intended uses:
+
+Named Entity Recognition (NER) tasks specifically for German.
+Token classification tasks involving German text.
+
+### Limitations:
+
+The model's performance is optimized for German and may not generalize well to other languages without further fine-tuning.
+The model's predictions are based on the data it was trained on and may not handle out-of-domain data as effectively.
 
 ## Training and evaluation data
 
-More information needed
+The model was fine-tuned on the German subset of the PAN-X dataset, which includes labeled examples of named entities in German text. The evaluation data is a separate portion of the same dataset, used to assess the model's performance.
 
 ## Training procedure
 
@@ -51,10 +59,13 @@ The following hyperparameters were used during training:
 | 0.1268        | 2.0   | 1050 | 0.1380          | 0.8503   |
 | 0.0794        | 3.0   | 1575 | 0.1363          | 0.8658   |
 
+### Evaluation results
+
+The model's F1-score on the validation set for the German subset is 0.8658, indicating a strong performance in named entity recognition for German text.
 
 ### Framework versions
 
 - Transformers 4.41.1
 - Pytorch 2.3.0+cu121
 - Datasets 2.19.1
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1