Instructions to use saeub/bert-stage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saeub/bert-stage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="saeub/bert-stage")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("saeub/bert-stage") model = AutoModelForTokenClassification.from_pretrained("saeub/bert-stage", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| language: | |
| - de | |
| # Statement Segmentation in German Easy Language (StaGE) submission | |
| This model is our submission to the [StaGE shared task](https://german-easy-to-read.github.io/statements/) at KONVENS 2024 under the team name *StaGE FriGHt*. It is based on [`bert-base-multilingual-cased`](https://huggingface.co/google-bert/bert-base-multilingual-cased) and fine-tuned for binary classification of statement span heads. The training data can be accessed [here](https://github.com/german-easy-to-read/statements/blob/master/data/train.csv). | |
| For more information, refer to the corresponding [GitHub repository](https://github.com/saeub/statement-segmentation), the paper about our submission (to be published) and the overview paper (to be published). |