Update README.md

README.md

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 pretty_name: DiscoEval Benchmar
 size_categories:
 - 10K<n<100K
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+---
+
+# DiscoEval Benchmark Datasets
+
+## Overview
+
+The DiscoEval benchmark datasets offer a collection of tasks designed to evaluate natural language understanding models in the context of discourse analysis and coherence. These tasks encompass various aspects of discourse comprehension and generation:
+
+1. **Sentence Positioning**
+   - **Datasets**: Arxiv, Wikipedia, Rocstory
+   - **Description**: Determine the correct placement of a sentence within a given context or document.
+
+2. **Binary Sentence Ordering**
+   - **Datasets**: Arxiv, Wikipedia, Rocstory
+   - **Description**: Choose between two possible orders for a pair of sentences, identifying the more coherent structure.
+
+3. **Discourse Coherence**
+   - **Datasets**: Ubuntu IRC channel, Wikipedia
+   - **Description**: Evaluate the coherence of a discourse or conversation by determining if a sequence of sentences forms a coherent conversation.
+
+4. **Sentence Section Prediction**
+   - **Dataset**: Constructed from PeerRead
+   - **Description**: Predict the section or category to which a sentence belongs within a scientific paper, based on the content and context.
+
+5. **Discourse Relations**
+   - **Datasets**: RST Discourse Treebank, Penn Discourse Treebank
+   - **Description**: Identify and classify discourse relations between sentences or text segments, helping to reveal the structure and flow of discourse.
+
+## Dataset Sources
+
+- **Arxiv**: A repository of scientific papers and research articles.
+- **Wikipedia**: An extensive online encyclopedia with articles on diverse topics.
+- **Rocstory**: A dataset consisting of fictional stories.
+- **Ubuntu IRC channel**: Conversational data extracted from the Ubuntu Internet Relay Chat (IRC) channel.
+- **PeerRead**: A dataset of scientific papers frequently used for discourse-related tasks.
+- **RST Discourse Treebank**: A dataset annotated with Rhetorical Structure Theory (RST) discourse relations.
+- **Penn Discourse Treebank**: Another dataset with annotated discourse relations, facilitating the study of discourse structure.
+
+## Use Cases
+
+The DiscoEval benchmark datasets are valuable for training and evaluating natural language understanding models, particularly those focusing on discourse analysis, document structure prediction, and text generation tasks.
+
+## Citation
+
+Ensure you cite the appropriate sources and papers associated with each dataset when using them in your research or applications.
+
+## License
+
+Each dataset within the DiscoEval benchmark may have distinct licensing terms. Always review and comply with the individual dataset's licensing terms and conditions when using them.
+
+## Loading Data Examples
+
+### Loading Data for Sentence Positioning Task
+
+```python
+from datasets import load_dataset
+
+# Load the Sentence Positioning dataset
+dataset = load_dataset("disco_eval/sentence_positioning")
+
+# Access the train, validation, and test splits
+train_data = dataset["train"]
+validation_data = dataset["validation"]
+test_data = dataset["test"]
+
+# Example usage: Print the first few training examples
+for example in train_data[:5]:
+    print(example)