Instructions to use kaanakdeniz/bert_base_uncased_grouped_textsim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaanakdeniz/bert_base_uncased_grouped_textsim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kaanakdeniz/bert_base_uncased_grouped_textsim")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kaanakdeniz/bert_base_uncased_grouped_textsim") model = AutoModelForSequenceClassification.from_pretrained("kaanakdeniz/bert_base_uncased_grouped_textsim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e41e06e5108f08fb90a194fd524ded041773812a2e4ca1818e653734274209da
- Size of remote file:
- 438 MB
- SHA256:
- e07410f8060f64a9cc64f4301957c94bbf22cdbffca33381aa5384c9a8f415f2
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