Instructions to use kaanakdeniz/bert_base_uncased_header_plus_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_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_zeroshot")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kaanakdeniz/bert_base_uncased_header_plus_zeroshot") model = AutoModelForSequenceClassification.from_pretrained("kaanakdeniz/bert_base_uncased_header_plus_zeroshot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd2bb8cc9ea869878205327eb48845d8824faeaa2314825a3599fc6161bf8f80
- Size of remote file:
- 438 MB
- SHA256:
- d71af01aee2ef82be30c653dbdc029c78064ce7f561aa419fb274943da61e6a6
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