Update Model Card

README.md

@@ -8,27 +8,30 @@ model-index:
   results: []
 ---
 
-<!-- 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-all
 
-This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the XTREME PANX dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.1758
 - F1 Score: 0.8558
 
 ## Model description
 
-More information needed
+This model is a fine-tuned version of xlm-roberta-base on a concatenated dataset combining multiple languages, specifically German (de) and French (fr). The model has been trained for token classification tasks and achieves competitive F1-scores across various languages.
+
+## Intended uses
+
+Named Entity Recognition (NER) tasks across multiple languages.
+Token classification tasks that benefit from multilingual training data.
 
-## Intended uses & limitations
+## Limitations
 
-More information needed
+Performance may vary on languages not seen during training.
+The model is fine-tuned on specific datasets and may require further fine-tuning or adjustments for other tasks or domains.
 
 ## Training and evaluation data
 
-More information needed
+The model was fine-tuned on a combination of German and French datasets, with the training data shuffled and concatenated to form a multilingual corpus. Additionally, the model was evaluated on multiple languages, showing robust performance across different linguistic datasets.
 
 ## Training procedure
 
@@ -51,6 +54,16 @@ The following hyperparameters were used during training:
 | 0.1587        | 2.0   | 1670 | 0.1705          | 0.8461   |
 | 0.1012        | 3.0   | 2505 | 0.1758          | 0.8558   |
 
+### Evaluation results
+
+The model was evaluated on multiple languages, achieving the following F1-scores:
+
+| Evaluated on  | de    | fr   |  it             | en       |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| Fine-tune on  |       |      |                 |          |
+| de            |0.8658 | 0.7021 | 0.6877        | 0.5830   |
+| each          |0.8658 | 0.8411 | 0.8180        | 0.6870   |
+| all           |0.8685 | 0.8654 | 0.8669        | 	0.7678  |
 
 ### Framework versions