Update README.md

README.md

@@ -10,7 +10,7 @@ widget:
 
 # mbart-finetuned-saf-micro-job
 
-This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the [saf_micro_job_german](https://huggingface.co/datasets/JohnnyBoy00/saf_micro_job_german) dataset for Short Answer Feedback (SAF), as proposed in [Filighera et al., ACL 2022](https://aclanthology.org/2022.acl-long.587).
+This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the [saf_micro_job_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_micro_job_german) dataset for Short Answer Feedback (SAF), as proposed in [Filighera et al., ACL 2022](https://aclanthology.org/2022.acl-long.587).
 
 ## Model description
 
@@ -39,7 +39,7 @@ It is important to acknowledge that the model underperforms when a question that
 
 ## Training and evaluation data
 
-As mentioned previously, the model was trained on the [saf_micro_job_german](https://huggingface.co/datasets/JohnnyBoy00/saf_micro_job_german) dataset, which is divided into the following splits.
+As mentioned previously, the model was trained on the [saf_micro_job_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_micro_job_german) dataset, which is divided into the following splits.
 
 | Split                 | Number of examples |
 | --------------------- | ------------------ |
@@ -97,8 +97,8 @@ The example below shows how the model can be applied to generate feedback to a g
 ```python
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-model = AutoModelForSeq2SeqLM.from_pretrained('JohnnyBoy00/mbart-finetuned-saf-micro-job')
-tokenizer = AutoTokenizer.from_pretrained('JohnnyBoy00/mbart-finetuned-saf-micro-job')
+model = AutoModelForSeq2SeqLM.from_pretrained('Short-Answer-Feedback/mbart-finetuned-saf-micro-job')
+tokenizer = AutoTokenizer.from_pretrained('Short-Answer-Feedback/mbart-finetuned-saf-micro-job')
 
 example_input = 'Antwort: Ich gebe mich zu erkennen und zeige das Informationsschreiben vor Lösung: Der Jobber soll sich in diesem Fall dem Personal gegenüber zu erkennen geben (0.25 P) und das entsprechende Informationsschreiben in der App vorzeigen (0.25 P). Zusätzlich muss notiert werden, zu welchem Zeitpunkt (0.25 P) des Jobs der Jobber enttarnt wurde. Zentrale Frage ist dabei, ob ein neutrales, unvoreingenommenes Verkaufsgespräch stattgefunden hat. Der Job soll mit Erlaubnis der Mitarbeiter bis zum Ende durchgeführt (0.25 P) werden. Frage: Frage 1: Wie reagierst du, wenn du auf deine Tätigkeit angesprochen wirst?'
 inputs = tokenizer(example_input, max_length=256, padding='max_length', truncation=True, return_tensors='pt')