Bert Base Therapist Topic Classification Eng

🚀 基于Bert-base-uncased的心理治疗话题分类模型

这是一个基于Bert-base-uncased微调的模型，用于对心理治疗场景中治疗师言论进行话题分类。该模型能够处理多分类任务，为心理治疗中的言论提供精准的话题标签。

🚀 快速开始

安装依赖

确保你已经安装了transformers库，如果没有安装，可以使用以下命令进行安装：

pip install transformers

模型调用示例

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import numpy as np

tokenizer = AutoTokenizer.from_pretrained("AIPsy/bert-base-therapist-topic-classification-eng")
model = AutoModelForSequenceClassification.from_pretrained("AIPsy/bert-base-therapist-topic-classification-eng")

text = "You know, I mean, it seems like you could just go to work and feel so much better."

encoding = tokenizer(
            text,
            truncation=True,
            padding="max_length",
            return_tensors="pt"
        )
output = model(encoding['input_ids'], encoding['attention_mask']).logits
result = np.argmax(output.detach().numpy(), axis=-1)
print(id2label[result[0]])
'Job Anxiety and Self-Reflection'

✨ 主要特性

• 多分类任务支持：该模型能够处理37个不同的话题分类，涵盖了心理治疗中常见的各种话题，如情感表达、人际关系、个人成长等。
• 基于预训练模型：基于Bert-base-uncased进行微调，充分利用了预训练模型的强大语义理解能力。

📦 安装指南

使用pip安装transformers库：

pip install transformers

💻 使用示例

基础用法

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import numpy as np

# 加载分词器和模型
tokenizer = AutoTokenizer.from_pretrained("AIPsy/bert-base-therapist-topic-classification-eng")
model = AutoModelForSequenceClassification.from_pretrained("AIPsy/bert-base-therapist-topic-classification-eng")

# 待分类的文本
text = "You know, I mean, it seems like you could just go to work and feel so much better."

# 对文本进行编码
encoding = tokenizer(
    text,
    truncation=True,
    padding="max_length",
    return_tensors="pt"
)

# 模型推理
output = model(encoding['input_ids'], encoding['attention_mask']).logits

# 获取分类结果
result = np.argmax(output.detach().numpy(), axis=-1)

# 打印分类结果
id2label = {0: 'Time Up and Future Meetings',
            1: 'Complex Emotions Toward Him',
            2: 'Desires and Disappointments',
            3: 'Personal Growth and Decision-Making',
            4: 'Self-Acceptance and Relationships',
            5: 'Understanding and Confronting Fear',
            6: 'See and Understanding Conversations',
            7: 'Clarifying Meaning and Intent',
            8: 'Desire to Escape and Leave',
            9: 'Uncertainty and Understanding Issues',
            10: 'Open Conversation and Sharing',
            11: 'Exploring Emotional Hurt and Bitterness',
            12: 'Guilt and Self-Blame Dynamics',
            13: 'Dynamics of Meaningful Relationships',
            14: 'Struggles and Desires in Learning',
            15: 'Gender Roles and Relationships',
            16: 'Struggles with Personal Change',
            17: 'Complex Mother-Sibling Relationships',
            18: 'Voices and Perception of Sound',
            19: 'Difficulties and Emotional Burdens',
            20: 'Fear and Reflection on Aging',
            21: 'Emotions of Crying and Tears',
            22: 'Father-Child Relationships and Authority',
            23: 'Possibilities and Potential Outcomes',
            24: 'Inner Struggle and Helplessness',
            25: 'Pursuing Meaningful Personal Goals',
            26: 'Job Anxiety and Self-Reflection',
            27: 'Marriage Anxiety and Dependence',
            28: 'Expressions of Anger and Frustration',
            29: 'Nurturing the inner child',
            30: 'Therapy and Father Relationships',
            31: 'Expressions of Happiness and Joy',
            32: 'Revisiting the Past Together',
            33: 'Drinking Habits and Concerns',
            34: 'Managing and Increasing Energy Levels',
            35: 'Understanding Depression and Its Roots',
            36: 'Nervous System and Stress Response'}

print(id2label[result[0]])

📚 详细文档

数据集

本模型的训练数据来源于YouTube上公开的心理治疗会话录音。经过说话人分割和转录后，共获得了15324个句子。

推荐使用场景

鉴于心理治疗会话中讨论问题的广泛背景，作者认为该模型可用于一般的人类交流分析。

评估指标

训练模型的得分指标

测试样本在各个类别上的F1得分指标

引用信息

• 论文：Vanin, A., Bolshev, V., & Panfilova, A. (2024). Applying LLM and Topic Modelling in Psychotherapeutic Contexts. ArXiv, abs/2412.17449. https://arxiv.org/abs/2412.17449
• 开发者：@myentity, @VadZhen, @Alek123
• 许可证：MIT

BibTeX：

@misc{vanin2024applyingllmtopicmodelling,
      title={Applying LLM and Topic Modelling in Psychotherapeutic Contexts}, 
      author={Alexander Vanin and Vadim Bolshev and Anastasia Panfilova},
      year={2024},
      eprint={2412.17449},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2412.17449}, 
}

📄 许可证

本项目采用MIT许可证。