XLM RoBERTa German Sentiment

🚀 XLM-RoBERTa-German-Sentiment

The XLM-RoBERTa-German-Sentiment model is designed for sentiment analysis in 8 languages, with a focus on German. It leverages the XLM-RoBERTa architecture and has been fine - tuned on a large German - language dataset.

🚀 Quick Start

To use this model, you need to install the Hugging Face Transformers library and PyTorch. You can do this using pip:

pip install torch transformers

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
text = "Erneuter Streik in der S-Bahn"
model = AutoModelForSequenceClassification.from_pretrained('ssary/XLM-RoBERTa-German-sentiment')
tokenizer = AutoTokenizer.from_pretrained('ssary/XLM-RoBERTa-German-sentiment')
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
    outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
sentiment_classes = ['negative', 'neutral', 'positive']
print(sentiment_classes[predictions.argmax()]) # for the class with highest probability
print(predictions) # for each class probability

✨ Features

• Multilingual Support: The model can perform sentiment analysis for 8 languages, including German, Arabic, French, Hindi, Italian, Portuguese, Spanish, and English.
• High Performance: Achieves an 87% Weighted F1 score.
• Tailored for German: Fine - tuned on over 200,000 German - language sentiment analysis samples.

📦 Installation

To use this model, you need to install the Hugging Face Transformers library and PyTorch. You can do this using pip:

pip install torch transformers

💻 Usage Examples

Basic Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
text = "Erneuter Streik in der S-Bahn"
model = AutoModelForSequenceClassification.from_pretrained('ssary/XLM-RoBERTa-German-sentiment')
tokenizer = AutoTokenizer.from_pretrained('ssary/XLM-RoBERTa-German-sentiment')
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
    outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
sentiment_classes = ['negative', 'neutral', 'positive']
print(sentiment_classes[predictions.argmax()]) # for the class with highest probability
print(predictions) # for each class probability

📚 Documentation

Overview

The XLM - RoBERTa - German - Sentiment model is designed to perform Sentiment Analysis for 8 Languages and more specifically German language. This model leverages the XLM - RoBERTa architecture, a choice inspired by the superior performance of Facebook's RoBERTa over Google's BERT across numerous benchmarks. The decision to use XLM - RoBERTa stems from its multilingual capabilities. Specifically tailored for the German language, this model has been fine - tuned on over 200,000 German - language sentiment analysis samples. More on the training of the model can be found in the paper.

The dataset utilized for training, available at [this GitHub repository](https://github.com/oliverguhr/german - sentiment - lib), was developed by Oliver Guhr. We extend our gratitude to him for making the dataset open source. The dataset was influential in refining the model's accuracy and responsiveness to the nuances of German sentiment.

Our model and finetuning is based on sentiment analysis model called xlm - t [https://arxiv.org/abs/2104.12250].

Model Details

🔧 Technical Details

The model is based on the XLM - RoBERTa architecture. It was fine - tuned on over 200,000 German - language sentiment analysis samples. The choice of XLM - RoBERTa was due to its multilingual capabilities and the superior performance of RoBERTa over BERT in many benchmarks. The model's finetuning is based on the sentiment analysis model xlm - t [https://arxiv.org/abs/2104.12250].

📄 License

This model is released under the Apache - 2.0 license.

Acknowledgments

This model was developed by Sary Nasser at HTW - Berlin under supervision of Martin Steinicke.

References

• Model's GitHub repository: [https://github.com/ssary/German - Sentiment - Analysis](https://github.com/ssary/German - Sentiment - Analysis)
• Oliver Guhr Dataset paper: [Training a Broad - Coverage German Sentiment Classification Model for Dialog Systems](http://www.lrec - conf.org/proceedings/lrec2020/pdf/2020.lrec - 1.202.pdf)
• Model architecture: XLM - T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond