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:
- 339d4e873dbeb17acebabcc8a2a9b5d40bf448ba50f8f2c85ddfc6a2691c4f5c
- Size of remote file:
- 438 MB
- SHA256:
- 512c008f486168f2aabef5f7c72bcfdd21faf35f47443d1b628d7f8be5ae6d74
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