Instructions to use veronica320/ADEPT_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use veronica320/ADEPT_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="veronica320/ADEPT_bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("veronica320/ADEPT_bert") model = AutoModelForSequenceClassification.from_pretrained("veronica320/ADEPT_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 238ca122b7a2de2491d1cdc5658f9b094b44f2d1ebbd4e1b7f656c484122c3d0
- Size of remote file:
- 438 MB
- SHA256:
- dcfb836d1ad97989bde67de361ffdad4d79bfa0f314159fe0ff1e6c93182c8f2
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