Update README.md

README.md

@@ -5,6 +5,8 @@ datasets:
 - wikipedia
 - natural_questions
 
+pipeline_tag: text2text-generation 
+
 license: apache-2.0
 ---
 
@@ -20,6 +22,18 @@ Paper: [How Much Knowledge Can You Pack
 Into the Parameters of a Language Model?](https://arxiv.org/abs/1910.10683.pdf)
 
 Authors: *Adam Roberts, Colin Raffel, Noam Shazeer* 
+
+## Results on Natural Questions - Open Test
+
+|Id | link | Exact Match  |
+|---|---|---|
+|**T5-small**|**https://huggingface.co/google/t5-small-ssm-nq**|**25.5**|
+|T5-large|https://huggingface.co/google/t5-large-ssm-nq|30.4|
+|T5-xl|https://huggingface.co/google/t5-xl-ssm-nq|35.6|
+|T5-xxl|https://huggingface.co/google/t5-xxl-ssm-nq|37.9|
+|T5-3b|https://huggingface.co/google/t5-3b-ssm-nq|33.2|
+|T5-11b|https://huggingface.co/google/t5-11b-ssm-nq|36.6|
+
 ## Usage
 
 The model can be used as follows for **closed book question answering**:
@@ -27,15 +41,13 @@ 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-small-ssm-nq")
-t5_tok = AutoTokenizer.from_pretrained("google/t5-small-ssm-nq")
+t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-large-ssm-nq")
+t5_tok = AutoTokenizer.from_pretrained("google/t5-large-ssm-nq")
 
 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 "1917" => not correct sadly.
 ```
 
 ## Abstract