Update README.md

README.md

@@ -5,6 +5,12 @@ tags:
 model-index:
 - name: distilbert-base-german-cased-finetuned-amazon-reviews
   results: []
+datasets:
+- amazon_reviews_multi
+language:
+- de
+metrics:
+- perplexity
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -12,21 +18,27 @@ should probably proofread and complete it, then remove this comment. -->
 
 # distilbert-base-german-cased-finetuned-amazon-reviews
 
-This model is a fine-tuned version of [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased) on an unknown dataset.
+This model is a fine-tuned version of [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased) on the Amazon Reviews multilingual dataset.
 It achieves the following results on the evaluation set:
 - Loss: 3.8874
 
+
 ## Model description
 
-More information needed
+The model is a fine-tuned version of distilbert-base-german-cased using the dataset from amazon_reviews_multi (available in Huggin Face). The purpose is to extend the distilbert-base-german-cased domain, which, once fine-tuned, will be modified for the fill-in-the-gaps task.
 
 ## Intended uses & limitations
 
-More information needed
+The use is limited to school use and the limitations have to do with the size of the dataset, since it does not allow for a large contribution, a larger dataset would have to be used to get a larger contribution.
 
 ## Training and evaluation data
 
-More information needed
+The training parameters are shown above.
+Evaluation: I used perplexity to evaluate the performance of my model:
+
+- Perplexity: 64.91
+
+The result is quite high, but the performance is quite good. 
 
 ## Training procedure
 
@@ -61,4 +73,4 @@ The following hyperparameters were used during training:
 
 - Transformers 4.26.1
 - Pytorch 1.13.1+cu116
-- Tokenizers 0.13.2
+- Tokenizers 0.13.2