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:
- 53b06f3e162892f73230c4b2b0f59bd848a71579e6829f9b5b7dfe27b72f6483
- Size of remote file:
- 438 MB
- SHA256:
- a345b295e369d300e87cc1d489e8f177fff177c86642ce6298292076816b9f79
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