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:
- 28ed392890f3e8a14823436e1cda2136cce9832606e8a8d9915ca0b66dfdb67f
- Size of remote file:
- 438 MB
- SHA256:
- 57d8543ea18d1298f82ce78c79fae2e76798411c083a22db5ce3793a546fb702
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