German Emotions

🚀 德语情感分类模型

这是一个用于德语情感分类的模型，它基于FacebookAI/xlm - roberta - base模型，使用go_emotions数据集的德语翻译版本进行微调。该模型能够对德语转录文本中的28种情感进行分类，为德语情感分析提供了有力支持。

🚀 快速开始

本模型可对德语转录文本中的28种情感进行分类。我们使用了go_emotions数据集，将其翻译成德语，并对FacebookAI/xlm - roberta - base模型进行了微调。该模型能够识别的28种情感包括：“钦佩”、“愉悦”、“愤怒”、“恼怒”、“认可”、“关心”、“困惑”、“好奇”、“渴望”、“失望”、“不认可”、“厌恶”、“尴尬”、“兴奋”、“恐惧”、“感激”、“悲痛”、“喜悦”、“爱”、“紧张”、“乐观”、“自豪”、“领悟”、“宽慰”、“懊悔”、“悲伤”、“惊讶”、“中立”。更多详细信息请参考文末发表的论文。

✨ 主要特性

• 语言支持：专注于德语情感分类，适用于德语转录文本。
• 情感种类丰富：能够识别28种不同的情感，满足多样化的情感分析需求。
• 模型基础：基于FacebookAI/xlm - roberta - base模型微调，具有较好的泛化能力。

📦 安装指南

在使用该模型前，你需要安装相关依赖库：

pip install transformers[torch]
pip install pandas, transformers, numpy, tqdm, openpyxl

💻 使用示例

基础用法

# pip install transformers[torch]
# pip install pandas, transformers, numpy, tqdm, openpyxl
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer
import numpy as np
from tqdm import tqdm
import time
import os
from transformers import DataCollatorWithPadding
import json

# create base path and input and output path for the model folder and the file folder
base_path = "/share/users/staff/c/clalk/Emotionen"
model_path = os.path.join(base_path, 'Modell')
file_path = os.path.join(base_path, 'Datensatz')

MODEL = "ChrisLalk/German-Emotions"
tokenizer = AutoTokenizer.from_pretrained(MODEL, do_lower_case=False)
model = AutoModelForSequenceClassification.from_pretrained(
    model_path,
    from_tf=False,
    from_flax=False,
    trust_remote_code=False,
    num_labels=28,
    ignore_mismatched_sizes=True
)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

# Path to the file
os.chdir(file_path)
df_full = pd.read_excel("speech_turns_pat.xlsx", index_col=None)

if 'Unnamed: 0' in df_full.columns:
    df_full = df_full.drop(columns=['Unnamed: 0'])

df_full.reset_index(drop=True, inplace=True)

# Tokenization and inference function
def infer_texts(texts):
    tokenized_texts = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
    class SimpleDataset:
        def __init__(self, tokenized_texts):
            self.tokenized_texts = tokenized_texts
        def __len__(self):
            return len(self.tokenized_texts["input_ids"])
        def __getitem__(self, idx):
            return {k: v[idx] for k, v in self.tokenized_texts.items()}
    test_dataset = SimpleDataset(tokenized_texts)
    trainer = Trainer(model=model, data_collator=data_collator)
    predictions = trainer.predict(test_dataset)
    sigmoid = torch.nn.Sigmoid()
    probs = sigmoid(torch.Tensor(predictions.predictions))
    return np.round(np.array(probs), 3).tolist()

start_time = time.time()
df = df_full

# Save results in a dict, here the df contains the additional variables File, Class, session, short_id, long_id, Prediction, hscl-11, and srs.
# However, only the "Sentence" column with the text is relevant for the pipeline. 
results = []
for index, row in tqdm(df.iterrows(), total=df.shape[0]):
    patient_texts = row['Patient']
    prob_list = infer_texts(patient_texts)
    results.append({
        "File": row['Class']+"_"+row['session'],
        "Class": row['Class'],
        "session": row['session'],
        "short_id": row["short_id"],
        "long_id": row["long_id"],
        "Sentence": patient_texts,
        "Prediction": prob_list[0],
        "hscl-11": row["Gesamtscore_hscl"],
        "srs": row["srs_ges"],
    })

# Convert results to df
df_results = pd.DataFrame(results)
df_results.to_json("emo_speech_turn_inference.json")

end_time = time.time()
elapsed_time = end_time - start_time
print(f"Elapsed time: {elapsed_time:.2f} seconds")
print(df_results)

emo_df = pd.DataFrame(df_results['Prediction'].tolist(), index=df_results["Class"].index)
col_names = ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral']
emo_df.columns = col_names
print(emo_df)

📚 详细文档

模型详情

分类指标

引用

使用本模型时，请引用相关的同行评审论文：

@article{Lalk2025EmotionDetection, 
  author = {Christopher Lalk and Kim Targan and Tobias Steinbrenner and Jana Schaffrath and Steffen Eberhardt and Brian Schwartz and Antonia Vehlen and Wolfgang Lutz and Julian Rubel}, 
  title = {Employing large language models for emotion detection in psychotherapy transcripts}, 
  journal = {Frontiers in Psychiatry}, 
  volume = {16}, 
  year = {2025}, 
  doi = {10.3389/fpsyt.2025.1504306}
}

📄 许可证

本模型使用的许可证为apache - 2.0。