Update README.md

README.md

@@ -4,6 +4,11 @@ language:
 - en
 paperswithcode_id: embedding-data/simple-wiki
 pretty_name: simple-wiki
+task_categories:
+- sentence-similarity
+- paraphrase-mining
+task_ids:
+- semantic-similarity-classification
 ---
 
 # Dataset Card for "simple-wiki"
@@ -41,14 +46,40 @@ pretty_name: simple-wiki
 - **Point of Contact:** [David Kauchak]([email protected])
 
 ### Dataset Summary
+This dataset contains pairs of equivalent sentences obtained from Wikipedia.
 
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://cs.pomona.edu/~dkauchak/simplification/)
-
+### Supported Tasks
+- [Sentence Transformers](https://huggingface.co/sentence-transformers) training; useful for semantic search and sentence similarity. 
 ### Languages
-
-[More Information Needed](https://cs.pomona.edu/~dkauchak/simplification/)
+- English.
+## Dataset Structure
+Each example in the dataset contains pairs of equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value".
+```
+{"set": [sentence_1, sentence_2]}
+{"set": [sentence_1, sentence_2]}
+...
+{"set": [sentence_1, sentence_2]}
+```
+This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using similar sentences.
+### Usage Example
+Install the 🤗 Datasets library with `pip install datasets` and load the dataset from the Hub with:
+```python
+from datasets import load_dataset
+dataset = load_dataset("embedding-data/simple-wiki")
+```
+The dataset is loaded as a `DatasetDict` and has the format:
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['set'],
+        num_rows: 102225
+    })
+})
+```
+Review an example `i` with:
+```python
+dataset["train"][i]["set"]
+```
 
 ### Curation Rationale
 
@@ -103,4 +134,3 @@ pretty_name: simple-wiki
 [More Information Needed](https://cs.pomona.edu/~dkauchak/simplification/)
 
 ### Contributions
-Thanks to [William Coster]([email protected]), [David Kauchak]([email protected]) for adding this dataset.