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:
- b5f7bb0fb8c7a127defb61fefbaaec0bd1409c59408abe928bab9edab67285a0
- Size of remote file:
- 3.31 kB
- SHA256:
- b7226c2835d0fb4b3748d34a497787b5dcadc92cfd11cf9e96254f3263a3dc94
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