Bert Base Therapist Topic Classification Eng

🚀 基於Bert-base-uncased的心理治療話題分類模型

這是一個基於Bert-base-uncased微調的模型，用於對心理治療場景中治療師言論進行話題分類。該模型能夠處理多分類任務，為心理治療中的言論提供精準的話題標籤。

🚀 快速開始

安裝依賴

確保你已經安裝了transformers庫，如果沒有安裝，可以使用以下命令進行安裝：

pip install transformers

模型調用示例

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

• 多分類任務支持：該模型能夠處理37個不同的話題分類，涵蓋了心理治療中常見的各種話題，如情感表達、人際關係、個人成長等。
• 基於預訓練模型：基於Bert-base-uncased進行微調，充分利用了預訓練模型的強大語義理解能力。

📦 安裝指南

使用pip安裝transformers庫：

pip install transformers

💻 使用示例

基礎用法

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import numpy as np

# 加載分詞器和模型
tokenizer = AutoTokenizer.from_pretrained("AIPsy/bert-base-therapist-topic-classification-eng")
model = AutoModelForSequenceClassification.from_pretrained("AIPsy/bert-base-therapist-topic-classification-eng")

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

# 對文本進行編碼
encoding = tokenizer(
    text,
    truncation=True,
    padding="max_length",
    return_tensors="pt"
)

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

# 獲取分類結果
result = np.argmax(output.detach().numpy(), axis=-1)

# 打印分類結果
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]])

📚 詳細文檔

數據集

本模型的訓練數據來源於YouTube上公開的心理治療會話錄音。經過說話人分割和轉錄後，共獲得了15324個句子。

推薦使用場景

鑑於心理治療會話中討論問題的廣泛背景，作者認為該模型可用於一般的人類交流分析。

評估指標

訓練模型的得分指標

測試樣本在各個類別上的F1得分指標

引用信息

• 論文：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
• 開發者：@myentity, @VadZhen, @Alek123
• 許可證：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}, 
}

📄 許可證

本項目採用MIT許可證。