Question Answering
Transformers
PyTorch
English
longformer
DocVQA
Document Question Answering
Document Visual Question Answering
Instructions to use rubentito/longformer-base-mpdocvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rubentito/longformer-base-mpdocvqa with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="rubentito/longformer-base-mpdocvqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("rubentito/longformer-base-mpdocvqa") model = AutoModelForQuestionAnswering.from_pretrained("rubentito/longformer-base-mpdocvqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rubentito/longformer-base-mpdocvqa: direct link, hf CLI and curl.
- Browser
- Download file 592 MB
-
https://huggingface.co/rubentito/longformer-base-mpdocvqa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rubentito/longformer-base-mpdocvqa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rubentito/longformer-base-mpdocvqa/resolve/main/pytorch_model.bin
592 MB
- Xet hash:
- ec78b3fb0374d19380615a8dbc9440e740260f78947ec869151b5587b44a9238
- Size of remote file:
- 592 MB
- SHA256:
- 7ad1a271d834c05918b71e4f7feaf8aa277e5134f9d960e5182b777dc9ded43c
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