Instructions to use wanggenfu/bert-textClassification-0.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wanggenfu/bert-textClassification-0.1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wanggenfu/bert-textClassification-0.1B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wanggenfu/bert-textClassification-0.1B") model = AutoModelForSequenceClassification.from_pretrained("wanggenfu/bert-textClassification-0.1B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e57be8a7b0aa60b73bf5955bba8dabb8ad03fa25f65a1e6abbe36080076bc3c
- Size of remote file:
- 438 MB
- SHA256:
- 36346f45fd85ffe14dfb3871922cba601ead7ecb3299a83b876be5ff06b1e6f0
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