Instructions to use JAlexis/bert003 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JAlexis/bert003 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="JAlexis/bert003")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("JAlexis/bert003") model = AutoModelForQuestionAnswering.from_pretrained("JAlexis/bert003", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38272e3dbe57eb4bad518219c9eb469bbfb3ae994c0d370c4f1ae6c3e2ec3266
- Size of remote file:
- 431 MB
- SHA256:
- 693db95c5475e70e9f8d05fd293dfd9df3c849e11af158c782b0aeff5e2688c3
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.