metadata

license: apache-2.0
datasets: google-research-datasets/go_emotions
base_model: FacebookAI/xlm-roberta-base
language:
  - de
metrics:
  - f1_macro: 0.45
  - accuracy: 0.41
  - kappa: 0.42
pipeline_tag: text-classification
tags:
  - medical
model_description: >-
  This model was fine-tuned on the German translation of the go_emotions
  dataset. It is designed to classify German text across 27 emotions (and a
  "neutral" category). The model is fine-tuned on the
  FacebookAI/xlm-roberta-base model.  It contains the following emotions:
  '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'.

Model Card for German-Emotions

German-Emotions

This model is designed to infer 27 emotions and a neutral category from German text. It is a fine-tuned version of FacebookAI/xlm-roberta-base, trained on the German translation of the GoEmotions dataset.

The original GoEmotions dataset contains 53.4k English Reddit comments labeled with one or more emotions. For this model, the data was translated into German and used to fine-tune the multilingual XLM-RoBERTa base model (270M parameters), which was pretrained on 2.5TB of CommonCrawl data across 100 languages, including German.

For additional information, please see the reference at the bottom of this page.

Supported Emotion Labels

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

Model Details

Model type: text-classification
Language(s) (NLP): German
License: apache-2.0
Finetuned from model: FacebookAI/xlm-roberta-base
Hyperparameters:
- Epochs: 10
- learning_rate: 3e-5
- weight_decay: 0.01
Metrics:
- accuracy: 0.41
- f1: 0.45
- kappa: 0.42

Classification Metrics

Emotion	Sentiment	F1	Cohen’s Kappa
admiration	positive	0.64	0.601
amusement	positive	0.78	0.767
anger	negative	0.38	0.358
annoyance	negative	0.27	0.229
approval	positive	0.34	0.293
caring	positive	0.38	0.365
confusion	negative	0.40	0.378
curiosity	positive	0.51	0.486
desire	positive	0.39	0.387
disappointment	negative	0.19	0.170
disapproval	negative	0.32	0.286
disgust	negative	0.41	0.395
embarrassment	negative	0.37	0.367
excitement	positive	0.35	0.339
fear	negative	0.59	0.584
gratitude	positive	0.89	0.882
grief	negative	0.31	0.307
joy	positive	0.51	0.499
love	positive	0.73	0.721
nervousness	negative	0.28	0.276
optimism	positive	0.53	0.512
pride	positive	0.30	0.299
realization	positive	0.17	0.150
relief	positive	0.27	0.266
remorse	negative	0.55	0.545
sadness	negative	0.50	0.488
surprise	neutral	0.53	0.514
neutral	neutral	0.60	0.410

How to Get Started with the Model

Use the code below to get started with the model.

import pandas as pd
from transformers import pipeline

# Example texts
texts = [
    "Ich fühle mich heute exzellent! Ich freue mich schon auf die Zeit mit meinen Freunden.",
    "Ich bin heute total müde und hab auf gar nichts Lust.",
    "Boah, das ist mir so peinlich.",
    "Hahaha, das ist so lustig."
]

# Create DataFrame
df = pd.DataFrame({"text": texts})

# Set labels
emotion_labels = ['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']

# Load emotion classifier pipeline
emo_pipe = pipeline(
    "text-classification",
    model="ChrisLalk/German-Emotions",  # or local model path
    tokenizer="ChrisLalk/German-Emotions",
    return_all_scores=True,
    truncation=True,
    top_k=None
)

# Infer the probability scores
prob_results = []
for text in df["text"]:
    scores = emo_pipe(text)[0]
    result_dict = {item["label"]: item["score"] for item in scores}
    result_dict_sort = {label: result_dict[label] for label in emotion_labels}
    prob_results.append(result_dict_sort)

# Add emotion scores to DataFrame
df_probs = pd.DataFrame(prob_results, columns=emotion_labels)
df_final = pd.concat([df, df_probs], axis=1)

Citation:

When using our model, please cite the associated peer-reviewed paper:

 bibtex @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}}