Instructions to use kaanakdeniz/bert_base_uncased_header_content_textsim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaanakdeniz/bert_base_uncased_header_content_textsim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kaanakdeniz/bert_base_uncased_header_content_textsim")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kaanakdeniz/bert_base_uncased_header_content_textsim") model = AutoModelForSequenceClassification.from_pretrained("kaanakdeniz/bert_base_uncased_header_content_textsim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd9dc61b97bfaab3ab266654d9c996ada87ee18e13289e2ced4771c69da8de9c
- Size of remote file:
- 3.31 kB
- SHA256:
- 9d0b78a4b5d4063508eafcde065a9b808b369380103122c7883b0e4259718af3
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