Update README.md

README.md

@@ -20,6 +20,33 @@ This model has been trained on the following datasets:
 
 # Use
 
+* Using the pipeline directly for end-to-end inference:
+```python
+from transformers import pipeline
+
+ate_sent_pipeline = pipeline(task='ner', 
+                  aggregation_strategy='simple',
+                  model="gauneg/roberta-base-absa-ate-sentiment")
+
+text_input = "Been here a few times and food has always been good but service really suffers when it gets crowded."
+ate_sent_pipeline(text_input)
+```
+* pipeline output:
+```bash
+[{'entity_group': 'pos', #sentiment polarity
+  'score': 0.8447307,
+  'word': ' food', # aspect term
+  'start': 26,
+  'end': 30},
+ {'entity_group': 'neg', #sentiment polarity
+  'score': 0.81927896,
+  'word': ' service', #aspect term
+  'start': 56,
+  'end': 63}]
+
+```
+
+# OR
 * Making token level inferences with Auto classes
 ```python
 from transformers import AutoTokenizer, AutoModelForTokenClassification
@@ -82,33 +109,7 @@ tok_levl_pred = list(zip(tokenizer.convert_ids_to_tokens(decoded_toks), decoded_
  ('.', 'O')]
 ```
 
-# OR
-
-* Using the pipeline directly for end-to-end inference:
-```python
-from transformers import pipeline
-
-ate_sent_pipeline = pipeline(task='ner', 
-                  aggregation_strategy='simple',
-                  model="gauneg/roberta-base-absa-ate-sentiment")
-
-text_input = "Been here a few times and food has always been good but service really suffers when it gets crowded."
-ate_sent_pipeline(text_input)
-```
-* pipeline output:
-```bash
-[{'entity_group': 'pos', #sentiment polarity
-  'score': 0.8447307,
-  'word': ' food', # aspect term
-  'start': 26,
-  'end': 30},
- {'entity_group': 'neg', #sentiment polarity
-  'score': 0.81927896,
-  'word': ' service', #aspect term
-  'start': 56,
-  'end': 63}]
 
-```
 
 # Evaluation on Benchmark Test Datasets