BrunoGR/Just_HEAR_Me

Sólo Escúchame: Spanish Emotional Accompaniment Chatbot 💬🤖

Sólo Escúchame is an open-source Spanish emotional assistance chatbot designed to provide psychological support. It is built upon the LLaMA-2-7b-Chat model and fine-tuned using the HEAR (Hispanic Emotional Accompaniment Responses) dataset.

Overview

Mental health issues have been rapidly increasing, with suicide being the fourth leading cause of death among individuals aged 15 to 29 in 2019, according to the World Health Organization (WHO). Sólo Escúchame aims to address this urgent need by offering a supplementary tool for psychological support, especially for Spanish speakers who may not have immediate access to professional help.

Features

• Emotional Assistance: Provides empathetic and supportive responses to users' emotional situations.
• HEAR Dataset: Trained on a specialized dataset for emotional accompaniment, compiled from multiple English sources and translated into Spanish.
• Open-Source: Available for public use and contribution, facilitating reproducibility and further research.
• CPU Efficient: Runs efficiently on CPUs, making it accessible to a wider audience.

Model

Sólo Escúchame is a fine-tuned version of the LLaMA-2-7b-Chat model. It utilizes the Rotary Positional Embedding (RoPE) and Grouped-Query Attention (GQA) techniques to enhance context length and model performance. The model has been quantized to 2, 4, and 8 bits to ensure accessibility.

Training

The model was trained using LoRA (Low Rank Adaptation) on the HEAR dataset. The training parameters were optimized for performance and efficiency.

Dataset

Hispanic Emotion Recognition Based on Plutchik’s Wheel (HRECPW) Dataset

• Source: Translated from diverse English sources including TweetEval, DailyDialog, HappyDB, and survey responses.
• Classes: 11 emotion classes - affection, happiness, admiration, anger, sadness, optimism, hate, surprise, fear, calm, and disgust.
• Size: 121,000 training examples, 2,200 validation examples, and 1,320 test examples.

Hispanic Emotional Accompaniment Responses (HEAR) Dataset

• Purpose: Used to train the Sólo Escúchame model for generating empathetic and suitable responses.
• Size: 41,481 training examples, 2,200 validation examples, and 1,320 test examples.

Evaluation

The model's performance was evaluated using two main criteria:

Active Listening Technique

Evaluation trait	GPT-3.5	LLaMA-2-7b-Chat	Mixtral8x7b	GPT-2-124M	Solo Escúchame
Contextual Attention	1256	1260	1277	462	1240
Clarifying Questions	776	718	531	199	913
Deeper Conversation	1215	1240	1185	470	1254
Absence of Judgment	1292	1278	1299	517	1300
Demonstration of Empathy	1246	1274	1287	502	1278

Socratic Method

Evaluation trait	GPT-3.5	LLaMA-2-7b-Chat	Mixtral8x7b	GPT-2-124M	Solo Escúchame
Use of Inductive Questions	1077	1033	872	502	1224
Non-Imposition of Ideas	1236	1170	1200	536	1299
Expansion and Construction of Knowledge	1031	1071	972	473	1245
Generation of Cognitive Dissonance	45	36	34	16	69
Guided Discovery	1089	1076	988	498	1253

Final Scores for Psychological Accompaniment Evaluation

Model	Active Listening	Socratic Method
GPT2-124M	32.57	30.68
Mixtral 8x7b	84.52	61.60
LLaMA-2-7b-Chat	87.42	66.45
GPT-3.5	87.62	67.84
Sólo Escúchame (ours)	90.67	77.12

Table: Final Scores for Psychological Accompaniment Evaluation in Language Models (LMs)

Usage

The Sólo Escúchame model and datasets are publicly available on Hugging Face:

• Model: Sólo Escúchame
• Datasets:
  • HRECPW Dataset
  • HEAR Dataset

Installation and Setup

To use the Sólo Escúchame model, follow these steps:

1. Clone the repository: git clone https://github.com/BrunoGilRamirez/Just_HEAR_ME
2. Install the required dependencies: pip install -r requirements.txt
3. Load the model and dataset from Hugging Face: from transformers import AutoModelForCausalLM, AutoTokenizer

License

Sólo Escúchame is released under the MIT License.

Citation

If you use Sólo Escúchame (Just_HEAR_Me) in your research, please cite the following paper:

@article{Gil2024,
  title={Sólo Escúchame: Spanish Emotional Accompaniment Chatbot},
  author={Gil Ramírez, Bruno and López Espejel, Jessica and Santiago Díaz, María del Carmen and Rubín Linares, Gustavo Trinidad},
  journal={arxiv},
  year={2024}
}

Contact

For any questions or inquiries, please contact:

• Bruno Gil Ramírez: [email protected]