Update README.md

README.md

@@ -34,4 +34,47 @@ configs:
     path: data/validation-*
   - split: test
     path: data/test-*
+license: apache-2.0
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- empathetic
+- ED
+- dialogue
+size_categories:
+- 10K<n<100K
 ---
+
+
+# Empathetic Dialogues for LLM
+
+This repository contains a reformatted version of the Empathetic Dialogues dataset, tailored for seamless integration with Language Model (LLM) training and inference. The original dataset's format posed challenges for direct application in LLM tasks, prompting us to restructure and clean the data.
+
+## Data Restructuring
+
+We have implemented the following changes to enhance the dataset's usability:
+
+1. Merged dialogues with the same `conv_id`, treating each `conv_id` as an independent dialogue session.
+2. Assigned the `user` role to the initiator of each dialogue session, followed by `assistant` for the subsequent message, and so on, alternating between the two roles.
+3. Retained the original `conv_id`, `emotion`, and `situation` fields to facilitate the construction of instructions.
+4. Removed the `utterance_id`, `selfeval`, and `tags` fields to streamline the data.
+5. Replaced instances of `'_comma_'` with `','` for improved readability.
+
+## Data Format
+
+Each entry in the reformatted dataset consists of the following fields:
+
+- `conversations`: A list of dictionaries, where each dictionary represents a turn in the dialogue and contains:
+- `role`: A string indicating the speaker's role, either `user` or `assistant`.
+- `content`: A string containing the dialogue content.
+- `conv_id`: A string representing the unique identifier for the dialogue session.
+- `emotion`: A string indicating the emotional label associated with the dialogue (corresponds to the `context` field in the original dataset).
+- `situation`: A string describing the situational label for the dialogue (corresponds to the `prompt` field in the original dataset).
+
+## Important Note
+
+In the original Empathetic Dialogues dataset, not all dialogue sessions have an even number of conversation turns. To maintain the integrity of the dataset, we have preserved this characteristic in our reformatted version. However, this peculiarity may lead to potential bugs when directly applying the dataset to LLM training or inference. Users should be mindful of this aspect when working with the data.
+
+