Update README.md

README.md

@@ -4,6 +4,11 @@ language:
 - en
 paperswithcode_id: embedding-data/QQP_triplets
 pretty_name: QQP_triplets
+task_categories:
+- sentence-similarity
+- paraphrase-mining
+task_ids:
+- semantic-similarity-classification
 
 ---
 
@@ -42,24 +47,46 @@ pretty_name: QQP_triplets
 
 ### Dataset Summary
 
-This dataset will give anyone the opportunity to train and test models of semantic equivalence, based on actual Quora data.
-
-The dataset consists of over 400,000 lines of potential question duplicate pairs. Each line contains IDs for each question in the pair, the full text for each question, and a binary value that indicates whether the line truly contains a duplicate pair.
+This dataset will give anyone the opportunity to train and test models of semantic equivalence, based on actual Quora data. The data is organized as triplets (anchor, positive, negative).
 
 Disclaimer: The team releasing Quora data did not upload the dataset to the Hub and did not write a dataset card. 
 These steps were done by the Hugging Face team.
 
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
+### Supported Tasks
+- [Sentence Transformers](https://huggingface.co/sentence-transformers) training; useful for semantic search and sentence similarity. 
 
 ### Languages
-
-[More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
+- English.
 
 ## Dataset Structure
-
-[More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)
+Each example is a dictionary with three keys (query, pos, and neg) containing a list each (triplets). The first key contains an anchor sentence, the second a positive sentence, and the third a list of negative sentences. 
+```
+{"query": [anchor], "pos": [positive], "neg": [negative1, negative2, ..., negativeN]}
+{"query": [anchor], "pos": [positive], "neg": [negative1, negative2, ..., negativeN]}
+...
+{"query": [anchor], "pos": [positive], "neg": [negative1, negative2, ..., negativeN]}
+```
+This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train them.
+
+### 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/QQP_triplets")
+```
+The dataset is loaded as a `DatasetDict` and has the format:
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['set'],
+        num_rows: 101762
+    })
+})
+```
+Review an example `i` with:
+```python
+dataset["train"][i]["set"]
+```
 
 ### Curation Rationale