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:
- a91124855ceab08d4f6d5d749f31387feb2a695280e54b512f4f3b56d02c7eae
- Size of remote file:
- 3.31 kB
- SHA256:
- db97c8cc1365b834410047fadcd0f0b2ecad0bda4eb4e881349d71f00a13aa95
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.