Instructions to use noahjadallah/cause-effect-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use noahjadallah/cause-effect-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="noahjadallah/cause-effect-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("noahjadallah/cause-effect-detection") model = AutoModelForTokenClassification.from_pretrained("noahjadallah/cause-effect-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| widget: | |
| - text: "If a user signs up, he will receive a confirmation email." | |
| # Cause-Effect Detection for Software Requirements Based on Token Classification with BERT | |
| This model uses BERT to detect cause and effect from a single sentence. The focus of this model is the domain of software requirements engineering, however, it can also be used for other domains. | |
| The model outputs one of the following 5 labels for each token: | |
| Other | |
| B-Cause | |
| I-Cause | |
| B-Effect | |
| I-Effect | |
| The source code can be found here: https://colab.research.google.com/drive/14V9Ooy3aNPsRfTK88krwsereia8cfSPc?usp=sharing |