Instructions to use bert-base/fun_trained_convbert_epoch_7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bert-base/fun_trained_convbert_epoch_7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bert-base/fun_trained_convbert_epoch_7")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bert-base/fun_trained_convbert_epoch_7") model = AutoModelForSequenceClassification.from_pretrained("bert-base/fun_trained_convbert_epoch_7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46e1b74858711b1481255be7b35f0b7bda1891567c4b35d76e673bf542352b98
- Size of remote file:
- 711 MB
- SHA256:
- ab314d508ecc78bfc50909cb2a9a49951358983c876f2bcaf0b339fdf4373053
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.