Instructions to use fathyshalab/bert-uncased-massive-intent-classification-banking-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fathyshalab/bert-uncased-massive-intent-classification-banking-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fathyshalab/bert-uncased-massive-intent-classification-banking-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fathyshalab/bert-uncased-massive-intent-classification-banking-1") model = AutoModelForSequenceClassification.from_pretrained("fathyshalab/bert-uncased-massive-intent-classification-banking-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b5dd8d60254db95e7eb2d5d50b58b2e812ceb3eff4c17a17be745af9c2618325
- Size of remote file:
- 438 MB
- SHA256:
- 1f342d75e233c011ce3648a10d4ebc6705fc1129e76f0759190e65d039a23f71
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