metadata

language:
  - en
license: apache-2.0
size_categories:
  - 1K<n<10K
task_categories:
  - text-generation
  - text-classification
dataset_info:
  features:
    - name: experience_type
      dtype: string
    - name: emotion_type
      dtype: string
    - name: problem_type
      dtype: string
    - name: situation
      dtype: string
    - name: survey_score
      struct:
        - name: seeker
          struct:
            - name: empathy
              dtype: string
            - name: final_emotion_intensity
              dtype: string
            - name: initial_emotion_intensity
              dtype: string
            - name: relevance
              dtype: string
        - name: supporter
          struct:
            - name: relevance
              dtype: string
    - name: seeker_question1
      dtype: string
    - name: seeker_question2
      dtype: string
    - name: supporter_question1
      dtype: string
    - name: supporter_question2
      dtype: string
    - name: conversations
      list:
        - name: content
          dtype: string
        - name: role
          dtype: string
        - name: strategy
          dtype: string
  splits:
    - name: train
      num_bytes: 3089088
      num_examples: 910
    - name: test
      num_bytes: 668538
      num_examples: 195
    - name: valid
      num_bytes: 663512
      num_examples: 195
  download_size: 2191251
  dataset_size: 4421138
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: valid
        path: data/valid-*
tags:
  - esconv
  - empathetic

ESCONV for LLM

This repository contains a reformatted version of the ESCONV 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

Assigned the user role to the usr, assistant role to the sys.
Removed the survey_scor and 'supporter' fields to streamline the data.

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.
- strategy: A string containing the strategy of current dialogue content, if role is user, strategy is NONE.
emotion: A string indicating the emotional label associated with the dialogue (corresponds to the emotion_type field in the original dataset).
situation: A string describing the situational label for the dialogue (corresponds to the situation field in the original dataset).
problem: A string describing the problem label for the user (corresponds to the problem_type field in the original dataset).
experience: A string, corresponds to the experience_type field in the original dataset.

Dataset Statistics

Dataset	Total Turn	Average Turn	Average Length
Train	26,648	29.284	14.547
Validation	5,678	29.118	14.630
Test	6,039	30.969	13.756