Update README.md

README.md

@@ -4,7 +4,11 @@ language:
 - en
 paperswithcode_id: embedding-data/Amazon-QA
 pretty_name: Amazon-QA
-
+task_categories:
+- sentence-similarity
+- paraphrase-mining
+task_ids:
+- semantic-similarity-classification
 ---
 
 # Dataset Card for "Amazon-QA"
@@ -45,49 +49,47 @@ pretty_name: Amazon-QA
 
 ### Dataset Summary
 
-This dataset contains Question and Answer data from Amazon, totaling around 1.4 million answered questions.
-
-This dataset can be combined with Amazon product review data, available [here](http://jmcauley.ucsd.edu/data/amazon/), 
-by matching ASINs in the Q/A dataset with ASINs in the review data. The review data also includes product metadata (product titles etc.).
+This dataset contains Question and Answer data from Amazon.
 
 Disclaimer: The team releasing Amazon-QA 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](http://jmcauley.ucsd.edu/data/amazon/qa/)
-
+### Supported Tasks
+- [Sentence Transformers](https://huggingface.co/sentence-transformers) training; useful for semantic search and sentence similarity. 
 ### Languages
-[More Information Needed](http://jmcauley.ucsd.edu/data/amazon/qa/)
-
+- English.
 ## Dataset Structure
-
+Each example in the dataset contains pairs of query and answer sentences and is formatted as a dictionary:
+```
+{"query": [sentence_1], "pos": [answer]}
+{"query": [sentence_1], "pos": [answer]}
+...
+{"query": [sentence_1], "pos": [answer]}
+```
+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/Amazon-QA")
+```
+The dataset is loaded as a `DatasetDict` and has the format:
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['query', 'pos'],
+        num_rows: 1095290
+    })
+})
+```
+Review an example `i` with:
+```python
+dataset["train"][0]
+```
 ### Data Instances
 
 ### Data Fields
 
-Sample question (and answer):
-
-```
-{
-  "asin": "B000050B6Z",
-  "questionType": "yes/no",
-  "answerType": "Y",
-  "answerTime": "Aug 8, 2014",
-  "unixTime": 1407481200,
-  "question": "Can you use this unit with GEL shaving cans?",
-  "answer": "Yes. If the can fits in the machine it will despense hot gel lather. I've been using my machine for both , gel and traditional lather for over 10 years."
-}
-```
-where
-
-asin - ID of the product, e.g. B000050B6Z
-questionType - type of question. Could be 'yes/no' or 'open-ended'
-answerType - type of answer. Could be 'Y', 'N', or '?' (if the polarity of the answer could not be predicted). Only present for yes/no questions.
-answerTime - raw answer timestamp
-unixTime - answer timestamp converted to unix time
-question - question text
-answer - answer text
 
 ### Data Splits
 
@@ -147,19 +149,9 @@ answer - answer text
 
 ### Citation Information
 
-```
-Modeling ambiguity, subjectivity, and diverging viewpoints in opinion question answering systems
-Mengting Wan, Julian McAuley
-International Conference on Data Mining (ICDM), 2016
 
-Addressing complex and subjective product-related queries with customer reviews
-Julian McAuley, Alex Yang
-World Wide Web (WWW), 2016
-
-```
 
 
 ### Contributions
 
-Thanks to [Julian McAuley](https://cseweb.ucsd.edu//~jmcauley/#) for adding this dataset.