Instructions to use Cameron/BERT-Jigsaw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cameron/BERT-Jigsaw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cameron/BERT-Jigsaw")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cameron/BERT-Jigsaw") model = AutoModelForSequenceClassification.from_pretrained("Cameron/BERT-Jigsaw", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 279e53df933641b09d5514ee4de075480c59d48e92a4c280f07410e411cfd482
- Size of remote file:
- 867 MB
- SHA256:
- e0ce995f76bc2f1767f84600752c24f6843097e936f637e4e54c12a7a6612bb6
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