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 tokenizer.json from rubentito/longformer-base-mpdocvqa: direct link, hf CLI and curl.
- Browser
- Download file 1.36 MB
-
https://huggingface.co/rubentito/longformer-base-mpdocvqa/resolve/main/tokenizer.json
- Command line
-
hf download hf://rubentito/longformer-base-mpdocvqa/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/rubentito/longformer-base-mpdocvqa/resolve/main/tokenizer.json
1.36 MB
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