Instructions to use OliverHeine/bert-large-uncased_fold_7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OliverHeine/bert-large-uncased_fold_7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/bert-large-uncased_fold_7")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OliverHeine/bert-large-uncased_fold_7") model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/bert-large-uncased_fold_7") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 317905391ee8f209d0a2e218ff8280e70a3788217b3a5ef31dd04e84e5828758
- Size of remote file:
- 5.84 kB
- SHA256:
- 41dd32a49c933dde38428e962bbf00548ecbf51fb69bed44740f717b044ec0fd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.