Instructions to use activebus/BERT_Review with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use activebus/BERT_Review with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="activebus/BERT_Review")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("activebus/BERT_Review") model = AutoModelForMaskedLM.from_pretrained("activebus/BERT_Review") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4134e7f29cb20768a89966b313c7e7f523e7bc7041212412257d7b67bbe470ca
- Size of remote file:
- 534 MB
- SHA256:
- c2bfac188d74394575890d5d5918741ae2bc4525e88f6417e764557391783042
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.