Update README.md

README.md

@@ -4,6 +4,11 @@ language:
 - en
 paperswithcode_id: embedding-data/SPECTER
 pretty_name: SPECTER
+task_categories:
+- sentence-similarity
+- paraphrase-mining
+task_ids:
+- semantic-similarity-classification
 ---
 
 # Dataset Card for "SPECTER"
@@ -41,39 +46,43 @@ pretty_name: SPECTER
 
 ### Dataset Summary
 
-SPECTER: Document-level Representation Learning using Citation-informed Transformers. 
-A new method to generate document-level embedding of scientific documents based on
-pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. 
-Unlike existing pretrained language models, SPECTER can be easily applied to 
-downstream applications without task-specific fine-tuning.
+Dataset containing triplets (three sentences): anchor, positive, and negative. Contains titles of papers.
 
 Disclaimer: The team releasing SPECTER 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://github.com/allenai/specter)
-
-### Languages
-
-[More Information Needed](https://github.com/allenai/specter)
-
 ## Dataset Structure
-Specter requires two main files as input to embed the document. 
-A text file with ids of the documents you want to embed and a json metadata file
-consisting of the title and abstract information. 
-Sample files are provided in the `data/` directory to get you started.
-Input data format is according to:
+Each example in the dataset contains triplets of equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value".
 
-metadata.json format:
+Each example is a dictionary with a key, "set", containing a list of three sentences (anchor, positive, and negative):
 
 ```
+{"set": [anchor, positive, negative]}
+{"set": [anchor, positive, negative]}
+...
+{"set": [anchor, positive, negative]}
+```
+This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using triplets.
 
-{
-    'doc_id': {'title': 'representation learning of scientific documents',
-               'abstract': 'we propose a new model for representing abstracts'},
-}
+### 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/SPECTER")
+```
+The dataset is loaded as a `DatasetDict` and has the format:
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['set'],
+        num_rows: 684100
+    })
+})
 ```
+Review an example `i` with:
+```python
+dataset["train"][i]["set"]
+
 
 ### Curation Rationale
 
@@ -129,24 +138,7 @@ metadata.json format:
 
 ### Citation Information
 
-```
-@inproceedings{specter2020cohan,
-  title={{SPECTER: Document-level Representation Learning using Citation-informed Transformers}},
-  author={Arman Cohan and Sergey Feldman and Iz Beltagy and Doug Downey and Daniel S. Weld},
-  booktitle={ACL},
-  year={2020}
-}
-
-```
-SciDocs benchmark
-
-SciDocs evaluation framework consists of a suite of evaluation tasks designed for document-level tasks.
-
-Link to SciDocs:
-
-- [https://github.com/allenai/scidocs](https://github.com/allenai/scidocs)
 
 
 ### Contributions
 
-Thanks to [@armancohan](https://github.com/armancohan), [@sergeyf](https://github.com/sergeyf), [@haroldrubio](https://github.com/haroldrubio), [@jinamshah](https://github.com/jinamshah) for adding this dataset.