Instructions to use ModelTC/bart-base-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bart-base-squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ModelTC/bart-base-squad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ModelTC/bart-base-squad") model = AutoModelForQuestionAnswering.from_pretrained("ModelTC/bart-base-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5bcc45bc1cf842c376d8b132f1536098df597856f3499ae5f6258857f8aa4356
- Size of remote file:
- 623 Bytes
- SHA256:
- 6ae6f423e03bb6a7ffc892c618affa609bc708ee76cd70a7fde72d43bf9569cb
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.