German Emotions

🚀 德語情感分類模型

這是一個用於德語情感分類的模型，它基於FacebookAI/xlm - roberta - base模型，使用go_emotions數據集的德語翻譯版本進行微調。該模型能夠對德語轉錄文本中的28種情感進行分類，為德語情感分析提供了有力支持。

🚀 快速開始

本模型可對德語轉錄文本中的28種情感進行分類。我們使用了go_emotions數據集，將其翻譯成德語，並對FacebookAI/xlm - roberta - base模型進行了微調。該模型能夠識別的28種情感包括：“欽佩”、“愉悅”、“憤怒”、“惱怒”、“認可”、“關心”、“困惑”、“好奇”、“渴望”、“失望”、“不認可”、“厭惡”、“尷尬”、“興奮”、“恐懼”、“感激”、“悲痛”、“喜悅”、“愛”、“緊張”、“樂觀”、“自豪”、“領悟”、“寬慰”、“懊悔”、“悲傷”、“驚訝”、“中立”。更多詳細信息請參考文末發表的論文。

✨ 主要特性

• 語言支持：專注於德語情感分類，適用於德語轉錄文本。
• 情感種類豐富：能夠識別28種不同的情感，滿足多樣化的情感分析需求。
• 模型基礎：基於FacebookAI/xlm - roberta - base模型微調，具有較好的泛化能力。

📦 安裝指南

在使用該模型前，你需要安裝相關依賴庫：

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

💻 使用示例

基礎用法

# 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)

📚 詳細文檔

模型詳情

分類指標

引用

使用本模型時，請引用相關的同行評審論文：

@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}
}

📄 許可證

本模型使用的許可證為apache - 2.0。