Instructions to use Tiejun/bert-base-banking77-pt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tiejun/bert-base-banking77-pt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tiejun/bert-base-banking77-pt2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tiejun/bert-base-banking77-pt2") model = AutoModelForSequenceClassification.from_pretrained("Tiejun/bert-base-banking77-pt2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3c521456d5cfb4b3d791e95eaa03ba2ced99e388fc4414284f43cfa5ce02f88
- Size of remote file:
- 3.96 kB
- SHA256:
- b6d885fc95ddf60bded0eff8cd09295c86a3138b71aeb68f280b4854a3120ec0
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