Instructions to use Bainbridge/bert-incl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bainbridge/bert-incl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Bainbridge/bert-incl")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Bainbridge/bert-incl") model = AutoModelForSequenceClassification.from_pretrained("Bainbridge/bert-incl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cfccba6858ac09654a5622d0cedcd889f11005282ed83d401121647b3fcfd0ff
- Size of remote file:
- 3.58 kB
- SHA256:
- ce6c919bc5efaa1cf5cf2493def1bbebe719cd33fe289173b780a4d9e32fece6
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