Update README.md

README.md

@@ -10,7 +10,7 @@ widget:
 
 # bart-finetuned-saf-communication-networks
 
-This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the [saf_communication_networks_english](https://huggingface.co/datasets/JohnnyBoy00/saf_communication_networks_english) 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/bart-large](https://huggingface.co/facebook/bart-large) on the [saf_communication_networks_english](https://huggingface.co/datasets/Short-Answer-Feedback/saf_communication_networks_english) 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_communication_networks_english](https://huggingface.co/datasets/JohnnyBoy00/saf_communication_networks_english) dataset, which is divided into the following splits.
+As mentioned previously, the model was trained on the [saf_communication_networks_english](https://huggingface.co/datasets/Short-Answer-Feedback/saf_communication_networks_english) dataset, which is divided into the following splits.
 
 | Split                 | Number of examples |
 | --------------------- | ------------------ |
@@ -96,8 +96,8 @@ 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/bart-finetuned-saf-communication-networks')
-tokenizer = AutoTokenizer.from_pretrained('JohnnyBoy00/bart-finetuned-saf-communication-networks')
+model = AutoModelForSeq2SeqLM.from_pretrained('Short-Answer-Feedback/bart-finetuned-saf-communication-networks')
+tokenizer = AutoTokenizer.from_pretrained('Short-Answer-Feedback/bart-finetuned-saf-communication-networks')
 example_input = 'Answer: In TCP there is a Sequence Number field to identify packets individually for reliability. There is no Sequence Number in UDP. The UDP header does not have an options field, while the TCP header does. In TCP there is an Advertised Window field for the Sliding Window Protocol for Flow Control. There is no Flow Control and therefore no Advertised Window field in UDP. In TCP there there is only a Data Offset field that specifies the header length. In UDP the whole Packet Length is transmitted. Reference: Possible Differences : The UPD header (8 bytes) is much shorter than the TCP header (20-60 bytes) The UDP header has a fixed length while the TCP header has a variable length Fields contained in the TCP header and not the UDP header : -Sequence number -Acknowledgment number -Reserved -Flags/Control bits -Advertised window -Urgent Pointer -Options + Padding if the options are UDP includes the packet length (data + header) while TCP has the header length/data offset (just header) field instead The sender port field is optional in UDP, while the source port in TCP is necessary to establish the connection Question: State at least 4 of the differences shown in the lecture between the UDP and TCP headers.'
 inputs = tokenizer(example_input, max_length=256, padding='max_length', truncation=True, return_tensors='pt')
 generated_tokens = model.generate(