Transformers
PyTorch
TensorBoard
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use ra4wv2/t5-large-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ra4wv2/t5-large-qa with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ra4wv2/t5-large-qa") model = AutoModelForSeq2SeqLM.from_pretrained("ra4wv2/t5-large-qa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee57ff7d71ae358941e2fc2de93ddbac57cf4330e6ad2cb3c23161914eacf9c8
- Size of remote file:
- 3.77 kB
- SHA256:
- 69a0c94fe03819050026b4eb0ae2314cf279bd535deb233cb522fb827dbd140d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.