Instructions to use kaanakdeniz/bert_base_uncased_header_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_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_textsim")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kaanakdeniz/bert_base_uncased_header_textsim") model = AutoModelForSequenceClassification.from_pretrained("kaanakdeniz/bert_base_uncased_header_textsim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c66e17ea491af5ad35e00c0663ec76f53b3683286863e0c063a1b5ecb282c35e
- Size of remote file:
- 3.31 kB
- SHA256:
- f847299780a007e1349be63a1ac463a7858229f101c507f83f348fa0dc5ce660
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