Instructions to use amjad101/bert-base-v2-args with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amjad101/bert-base-v2-args with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amjad101/bert-base-v2-args")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amjad101/bert-base-v2-args") model = AutoModelForSequenceClassification.from_pretrained("amjad101/bert-base-v2-args") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 643b2a13c8fedcf75d981f89ce7f9f18d05229e84ee54f44843623c91b932c5a
- Size of remote file:
- 438 MB
- SHA256:
- f4dff17612343390e3fb404836a8b82baeec8afe3558ec8a6e34252c7d36135a
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