Instructions to use kaanakdeniz/bert_base_uncased_header_plus_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_plus_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_plus_content_textsim")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kaanakdeniz/bert_base_uncased_header_plus_content_textsim") model = AutoModelForSequenceClassification.from_pretrained("kaanakdeniz/bert_base_uncased_header_plus_content_textsim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a2453916d3873af23efd82d6eef1146b01db302d7b2d42283d2302ae5a3e8f9
- Size of remote file:
- 3.31 kB
- SHA256:
- 6530c93893d67fdd0c21ecfb6fc18813ef30309dbb3fed5817b38de076b10991
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