Question Answering
Transformers
PyTorch
English
big_bird
DocVQA
Document Question Answering
Document Visual Question Answering
Instructions to use rubentito/bigbird-base-itc-mpdocvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rubentito/bigbird-base-itc-mpdocvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="rubentito/bigbird-base-itc-mpdocvqa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("rubentito/bigbird-base-itc-mpdocvqa") model = AutoModelForQuestionAnswering.from_pretrained("rubentito/bigbird-base-itc-mpdocvqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 623442c374582bc5bedc44187b9fd53e7e8cedee589a39f74ace7ef6655bce14
- Size of remote file:
- 527 MB
- SHA256:
- b4398d8519ebac016278d81ba9db14b0141205eafb26ce7d5ee54bc9565f5eaf
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