Instructions to use g8a9/bert-base-cased_ami18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use g8a9/bert-base-cased_ami18 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="g8a9/bert-base-cased_ami18")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("g8a9/bert-base-cased_ami18") model = AutoModelForSequenceClassification.from_pretrained("g8a9/bert-base-cased_ami18") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 35f08b98e7a7eba12002e7ad7a7618e87acf5e737f808a815d3ed5dcf2237675
- Size of remote file:
- 433 MB
- SHA256:
- edacb0289d15ffef2ece337701fa6aafe1adb94a6d8d9ca6c798ed5bcaffe879
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