Transformers
PyTorch
English
t5
text2text-generation
DocVQA
Document Question Answering
Document Visual Question Answering
text-generation-inference
Instructions to use rubentito/t5-base-mpdocvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rubentito/t5-base-mpdocvqa with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rubentito/t5-base-mpdocvqa") model = AutoModelForSeq2SeqLM.from_pretrained("rubentito/t5-base-mpdocvqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f7057bfef3c6b64869a641b26451b8775db16b1a6ec890ec97c86007b4c943d7
- Size of remote file:
- 892 MB
- SHA256:
- 0a7fb3389089cf26f991ab60e8b5b972d15d54c2d40f4618d059e87059efc706
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.