Update README.md

README.md

@@ -1,4 +1,16 @@
 ---
+language:
+- en
+multilinguality:
+- monolingual
+size_categories:
+- 100M<n<1B
+task_categories:
+- feature-extraction
+- sentence-similarity
+pretty_name: S2ORC
+tags:
+- sentence-transformers
 dataset_info:
 - config_name: abstract-citation-pair
   features:
@@ -65,6 +77,7 @@ configs:
   data_files:
   - split: train
     path: title-abstract-pair/train-*
+  default: true
 - config_name: title-citation-pair
   data_files:
   - split: train
@@ -74,3 +87,67 @@ configs:
   - split: train
     path: title-citation-pair-all/train-*
 ---
+
+# Dataset Card for S2ORC
+
+This dataset contains titles, abstracts, and citations from scientific papers from the [Semantic Scholar Open Research Corpus (S2ORC)](https://github.com/allenai/s2orc).
+This dataset can and has been used to train embedding models, and works out of the box to train or finetune [Sentence Transformer](https://sbert.net/) models.
+
+In our experiments, title-abstract pairs result in the highest performance, followed by titles-citations and then abstract-citations pairs.
+
+## Dataset Subsets
+
+### `title-abstract-pair` subset
+
+* Columns: "title", "abstract"
+* Column types: `str`, `str`
+* Examples:
+    ```python
+    
+    ```
+* Collection strategy: Reading the S2ORC titles-abstract dataset from [embedding-training-data](https://huggingface.co/datasets/sentence-transformers/embedding-training-data).
+* Deduplified: No
+
+### `title-citation-pair` subset
+
+* Columns: "title", "citation"
+* Column types: `str`, `str`
+* Examples:
+    ```python
+    
+    ```
+* Collection strategy: Reading the S2ORC titles-citation dataset from [embedding-training-data](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) and considering each title together with the first citation as a sample.
+* Deduplified: No
+
+### `title-citation-pair-all` subset
+
+* Columns: "title", "citation"
+* Column types: `str`, `str`
+* Examples:
+    ```python
+    
+    ```
+* Collection strategy: Reading the S2ORC titles-citation dataset from [embedding-training-data](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) and considering each title together with each citation as a sample.
+* Deduplified: No
+
+### `abstract-citation-pair` subset
+
+* Columns: "abstract", "citation"
+* Column types: `str`, `str`
+* Examples:
+    ```python
+    
+    ```
+* Collection strategy: Reading the S2ORC abstract-citation dataset from [embedding-training-data](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) and considering each citation together with the first abstract as a sample.
+* Deduplified: No
+
+### `abstract-citation-pair-all` subset
+
+* Columns: "abstract", "citation"
+* Column types: `str`, `str`
+* Examples:
+    ```python
+    
+    ```
+* Collection strategy: Reading the S2ORC abstract-citation dataset from [embedding-training-data](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) and considering each citation together with each abstract as a sample.
+* Deduplified: No