Instructions to use kaanakdeniz/bert_base_uncased_header_plus_content_zeroshot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaanakdeniz/bert_base_uncased_header_plus_content_zeroshot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kaanakdeniz/bert_base_uncased_header_plus_content_zeroshot")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kaanakdeniz/bert_base_uncased_header_plus_content_zeroshot") model = AutoModelForSequenceClassification.from_pretrained("kaanakdeniz/bert_base_uncased_header_plus_content_zeroshot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20bb5df6f8b447c8c9c39339bd020d8b27acf924b3779fabb82aa402b9276dac
- Size of remote file:
- 438 MB
- SHA256:
- 03f756cb7606859cbe46b5950ea01aef180e0a99153375de3d70003c41044bdd
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