Instructions to use mac999/squad_qa_custom_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mac999/squad_qa_custom_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="mac999/squad_qa_custom_model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mac999/squad_qa_custom_model") model = AutoModelForQuestionAnswering.from_pretrained("mac999/squad_qa_custom_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9cebf0b04d7aa3ecba20770aa2aa890cd5df014fd5d241d3b4fa3f94052540e4
- Size of remote file:
- 5.3 kB
- SHA256:
- a155c86ee71379a47f24d48d0470c37e9a302cda005d0707055708e6ad3bf057
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.