Update README.md

README.md

@@ -9,28 +9,19 @@ library_name: transformers
 
 # LLäMmlein 120M
 
-This is a German Tinyllama 120M language model trained from scratch using the 
-the [Tinyllama](https://github.com/jzhang38/TinyLlama) codebase on the German portion of [RedPajama V2](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2). 
-
+This is a German Tinyllama 120M language model trained from scratch using the [Tinyllama](https://github.com/jzhang38/TinyLlama) codebase on the German portion of [RedPajama V2](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2). 
+Find more details on our [page](https://www.informatik.uni-wuerzburg.de/datascience/projects/nlp/llammlein/)!
 
 ### Usage
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model = AutoModelForCausalLM.from_pretrained("LSX-UniWue/LLaMmlein_120m")
+model = AutoModelForCausalLM.from_pretrained("LSX-UniWue/LLaMmlein_120M")
 
-tokenizer = AutoTokenizer.from_pretrained("LSX-UniWue/LLaMmlein_120m")
+tokenizer = AutoTokenizer.from_pretrained("LSX-UniWue/LLaMmlein_120M")
 ```
 
 
 ### Performance 
-We evaluated our model on the [SuperGLEBer](https://lsx-uniwue.github.io/SuperGLEBer-site/) benchmark. 
-
-| Task Type           | Task Name    | Metric   | Score |
-|---------------------|--------------|----------|-------|
-| Classification      | NLI          | Accuracy | 0.629 | 
-| Classification      | DB Aspect    | micro F1 | 0.517 | 
-| Sequence Tagging    | NER Europarl | micro F1 | 0.538 | 
-| Sentence Similarity | Pawsx        | Pearson  | 0.489 | 
-| Question Answering  | MLQA         | F1       | 0.846 |
+We evaluated our model on the [SuperGLEBer](https://lsx-uniwue.github.io/SuperGLEBer-site/) benchmark.