Instructions to use wiorz/bert_small_summarized_defined with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wiorz/bert_small_summarized_defined with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wiorz/bert_small_summarized_defined")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wiorz/bert_small_summarized_defined") model = AutoModelForSequenceClassification.from_pretrained("wiorz/bert_small_summarized_defined") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 05bd6ecb3b60fa3371a1f55bcb89615fb4331bd26ac3438d43568fa6201b198c
- Size of remote file:
- 438 MB
- SHA256:
- 0792a933f25d46c8a0a495ec2ccaaf03b09548e36bc6b79f68ff6a83ba5f74e7
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