Update README.md

README.md

@@ -41,20 +41,6 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-```
-from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
-model = AutoModelForTokenClassification.from_pretrained('kalexa2/fabner-ner')
-tokenizer = AutoTokenizer.from_pretrained('kalexa2/fabner-ner')
-token_classifier = pipeline('ner',
-               model=model,
-               tokenizer=tokenizer,
-               aggregation_strategy="simple" )
-r = token_classifier("Here, we report in-situ characterization of melt-flow dynamics in every location of the entire melt pool in laser metal additive manufacturing by populous and uniformly dispersed micro-tracers through in-situ high-resolution synchrotron x-ray imaging .")
- 
-for entity in r:
-    print(entity) 
-```
-
 
 [More Information Needed]
 
@@ -86,6 +72,22 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 Use the code below to get started with the model.
 
+
+```
+from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
+model = AutoModelForTokenClassification.from_pretrained('kalexa2/fabner-ner')
+tokenizer = AutoTokenizer.from_pretrained('kalexa2/fabner-ner')
+token_classifier = pipeline('ner',
+               model=model,
+               tokenizer=tokenizer,
+               aggregation_strategy="simple" )
+r = token_classifier("Here, we report in-situ characterization of melt-flow dynamics in every location of the entire melt pool in laser metal additive manufacturing by populous and uniformly dispersed micro-tracers through in-situ high-resolution synchrotron x-ray imaging .")
+ 
+for entity in r:
+    print(entity) 
+```
+
+
 [More Information Needed]
 
 ## Training Details