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:
- cf00ce471651258e43ea32d9a8fbfdada0d27ced524fa4589406ccbcbe31053d
- Size of remote file:
- 438 MB
- SHA256:
- e995c9958acf8c375ce415af0eb5299397955922e2934d039717599486b53773
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