Update README.md

README.md

@@ -32,6 +32,48 @@ This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment
 
 The text classification task in this model is based on 3 sentiment labels.
 
+## Full classification example:
+
+```python
+from transformers import pipeline
+
+pipe = pipeline(model="delarosajav95/tw-roberta-base-sentiment-FT")
+inputs = ["The flat is very nice but it's too expensive and the location is very bad.",
+          "I loved the music, but the crowd was too rowdy to enjoy it properly.",
+          "They believe that I'm stupid and I like waiting for hours in line to buy a simple coffee."
+]
+result = pipe(inputs, return_all_scores=True)
+
+label_mapping = {"LABEL_0": "Negative", "LABEL_1": "Neutral", "LABEL_2": "Positive"}
+for i, predictions in enumerate(result):
+  print("==================================")
+  print(f"Text {i + 1}: {inputs[i]}")
+  for pred in predictions:
+    label = label_mapping.get(pred['label'], pred['label'])
+    score = pred['score']
+    print(f"{label}: {score:.2%}")
+```
+
+Output:
+
+```python
+==================================
+Text 1: The flat is very nice but it's too expensive and the location is very bad.
+Negative: 0.09%
+Neutral: 99.88%
+Positive: 0.03%
+==================================
+Text 2: I loved the music, but the crowd was too rowdy to enjoy it properly.
+Negative: 0.04%
+Neutral: 99.92%
+Positive: 0.04%
+==================================
+Text 3: They believe that I'm stupid and I like waiting for hours in line to buy a simple coffee.
+Negative: 69.79%
+Neutral: 30.12%
+Positive: 0.09%
+```
+
 ## Metrics and results:
 
 It achieves the following results on the evaluation set:
@@ -66,6 +108,7 @@ The following hyperparameters were used during training:
 
 ## CITATION:
 
+```bibitex 
 @inproceedings{barbieri-etal-2020-tweeteval,
     title = "{T}weet{E}val: Unified Benchmark and Comparative Evaluation for Tweet Classification",
     author = "Barbieri, Francesco  and
@@ -81,7 +124,7 @@ The following hyperparameters were used during training:
     doi = "10.18653/v1/2020.findings-emnlp.148",
     pages = "1644--1650"
 }
-
+```
 
 ## More Information