Update README.md

README.md

@@ -13,6 +13,7 @@ license: apache-2.0
 The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.pdf)'s salient span masking objective on [Wikipedia](https://huggingface.co/datasets/wikipedia), and finally fine-tuned on [Natural Questions (NQ)](https://huggingface.co/datasets/natural_questions).
 
 **Note**: The model was fine-tuned on 90% of the train splits of [Natural Questions (NQ)](https://huggingface.co/datasets/natural_questions) for 20k steps.
+
 Other community Checkpoints: [here](https://huggingface.co/models?search=ssm)
 
 Paper: [How Much Knowledge Can You Pack
@@ -26,15 +27,15 @@ The model can be used as follows for **closed book question answering**:
 ```python
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-large-ssm-nq")
-t5_tok = AutoTokenizer.from_pretrained("google/t5-large-ssm-nq")
+t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-large-ssm-nqo")
+t5_tok = AutoTokenizer.from_pretrained("google/t5-large-ssm-nqo")
 
 input_ids = t5_tok("When was Franklin D. Roosevelt born?", return_tensors="pt").input_ids
 gen_output = t5_qa_model.generate(input_ids)[0]
 
 print(t5_tok.decode(gen_output, skip_special_tokens=True))
 
-# should give "December 26, 1892" => close, but not correct.
+# should give "On February 13, 1904" => not correct sadly.
 ```
 
 ## Abstract