Instructions to use brad1141/bertBasev2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brad1141/bertBasev2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="brad1141/bertBasev2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("brad1141/bertBasev2") model = AutoModelForSequenceClassification.from_pretrained("brad1141/bertBasev2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e7df375a6167754aa9d2568a6afc9f824e9f3bf581abaeadb85b1a898b9fac74
- Size of remote file:
- 433 MB
- SHA256:
- d9c4afd503df31d451f710947d79fc5fdf523ebcc49c6575f2fe3d2628223eb2
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