Update README.md

README.md

@@ -20,6 +20,37 @@ This model has been trained on the following datasets:
 2. Multi-Aspect Multi-Sentiment [MAMS](https://aclanthology.org/D19-1654/)
 
 # Use
+* Making end-to-end inference with a pipeline
+
+```python
+
+from transformers import pipeline
+
+ate_sent_pipeline = pipeline(task='ner', 
+                  aggregation_strategy='simple',
+                  model="gauneg/deberta-v3-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)
+
+```
+Expected output
+
+```bash
+[{'entity_group': 'pos', #sentiment polarity
+  'score': 0.87505656,
+  'word': 'food', # aspect term
+  'start': 25,
+  'end': 30},
+ {'entity_group': 'neg',# sentiment polarity
+  'score': 0.4558051,
+  'word': 'service', #aspect term
+  'start': 55,
+  'end': 63}]
+
+```
+
+# OR
 
 * Making token level inferences with Auto classes
 
@@ -82,35 +113,7 @@ Expected output
  ('.', 'O')]
 ```
 
-* Making end-to-end inference with a pipeline
-
-```python
-
-from transformers import pipeline
 
-ate_sent_pipeline = pipeline(task='ner', 
-                  aggregation_strategy='simple',
-                  model="gauneg/deberta-v3-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)
-
-```
-Expected output
-
-```bash
-[{'entity_group': 'pos', #sentiment polarity
-  'score': 0.87505656,
-  'word': 'food', # aspect term
-  'start': 25,
-  'end': 30},
- {'entity_group': 'neg',# sentiment polarity
-  'score': 0.4558051,
-  'word': 'service', #aspect term
-  'start': 55,
-  'end': 63}]
-
-```
 
 
 # Evaluation on Benchmark Test Datasets