Update README.md

README.md

@@ -1,3 +1,18 @@
+---
+inference:
+  parameters:
+    do_sample: True
+    max_length: 500
+    top_p: 0.9
+    top_k: 20
+    temperature: 1
+    num_return_sequences: 10
+    
+widget:
+- text: "abstract: We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
+  example_title: "BERT abstract"
+---
+
 ```
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
@@ -22,20 +37,4 @@ for i, output in enumerate(outputs):
     print("{}: {}".format(i+1, tokenizer.decode(output, skip_special_tokens=True)))
 
 ```
-
-GitHub: https://github.com/ArvinZhuang/BiTAG
-
----
-inference:
-  parameters:
-    do_sample: True
-    max_length: 500
-    top_p: 0.9
-    top_k: 20
-    temperature: 1
-    num_return_sequences: 10
-    
-widget:
-- text: "abstract: We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
-  example_title: "BERT abstract"
----
+GitHub: https://github.com/ArvinZhuang/BiTAG