Instructions to use samheym/GerCross-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samheym/GerCross-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="samheym/GerCross-Encoder")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("samheym/GerCross-Encoder") model = AutoModelForSequenceClassification.from_pretrained("samheym/GerCross-Encoder") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| tags: | |
| - cross-encoder | |
| ## Model Description | |
| This model was trained as part of a Bachelor's thesis evaluating and comparing different retrieval architectures, specifically ColBERT, Cross-Encoders, and Bi-Encoders. The primary focus of this work was to assess retrieval effectiveness in a German-language information retrieval setting. | |
| Intended Use | |
| The model is intended for research and experimentation in the field of Information Retrieval (IR). It can be used to analyze retrieval quality in contrast to other architectures or as a reference for future work on ColBERT in German. | |
| Limitations & Warnings | |
| ## ⚠ Not for Production Use | |
| This model is experimental and has not been optimized for production use. Performance, robustness, and scalability considerations have not been fully addressed, and real-world deployment is strongly discouraged. | |