Instructions to use kelingwang/bert-causation-rating-dgt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kelingwang/bert-causation-rating-dgt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kelingwang/bert-causation-rating-dgt")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kelingwang/bert-causation-rating-dgt") model = AutoModelForSequenceClassification.from_pretrained("kelingwang/bert-causation-rating-dgt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5c88133b2ca373ac6966f3366039258fa0993abaa3372b8f439d46421b3d69a
- Size of remote file:
- 433 MB
- SHA256:
- 7b02d1aaf646c5bc43a676d8ff459c663ac391e81c13c69929e84abc3062566e
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