Instructions to use kaanakdeniz/bert_base_uncased_header_plus_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_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_textsim")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kaanakdeniz/bert_base_uncased_header_plus_textsim") model = AutoModelForSequenceClassification.from_pretrained("kaanakdeniz/bert_base_uncased_header_plus_textsim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ac233c7f6ab5400bc308d72e78d493ec0f4553491da3969e973ed1ee5d4e699d
- Size of remote file:
- 438 MB
- SHA256:
- 3980cc37760802dfbb8d54f46181bf7c26ebcb958ed5b322589b64c411ed5a21
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