Update README.md

Added data usage and quick baseline examples along with some minor improvements to the README.md

README.md

@@ -149,7 +149,7 @@ English
 ```
 
 ### Data Fields
-
+Each example contains:
 - sentence: a tokenized line from the dataset
 - label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral'
 
@@ -158,10 +158,37 @@ There's no train/validation/test split.
 
 However the dataset is available in four possible configurations depending on the percentage of agreement of annotators:
 
-`sentences_50agree`; Number of instances with >=50% annotator agreement: 4846 
-`sentences_66agree`: Number of instances with >=66% annotator agreement: 4217
-`sentences_75agree`: Number of instances with >=75% annotator agreement: 3453
-`sentences_allagree`: Number of instances with 100% annotator agreement: 2264
+- `sentences_50agree`; Number of instances with >=50% annotator agreement: 4846 
+- `sentences_66agree`: Number of instances with >=66% annotator agreement: 4217
+- `sentences_75agree`: Number of instances with >=75% annotator agreement: 3453
+- `sentences_allagree`: Number of instances with 100% annotator agreement: 2264
+
+### Usage
+
+```python
+from datasets import load_dataset
+
+# Load the highest-agreement configuration
+ds = load_dataset("takala/financial_phrasebank", "sentences_allagree")
+
+print(ds)
+print(ds["train"][0])
+```
+
+Other configurations (e.g. `sentences_75agree`, `sentences_66agree`) can be loaded by changing the second argument.
+
+## Quick baseline (Transformers)
+
+```python
+from datasets import load_dataset
+from transformers import pipeline
+
+ds = load_dataset("takala/financial_phrasebank", "sentences_allagree")["train"]
+clf = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+print(ds[0]["sentence"])
+print(clf(ds[0]["sentence"]))
+```
 
 ## Dataset Creation