Question Answering
Transformers
PyTorch
TensorBoard
English
t5
text2text-generation
text-generation-inference
Instructions to use grasgor/flan-t5-small-wikitablequestions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use grasgor/flan-t5-small-wikitablequestions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="grasgor/flan-t5-small-wikitablequestions")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("grasgor/flan-t5-small-wikitablequestions") model = AutoModelForSeq2SeqLM.from_pretrained("grasgor/flan-t5-small-wikitablequestions") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 627c873b8d66fd31111bb77b97b5f0d9463529bbf7a1295e3a34acc5a4ac2b13
- Size of remote file:
- 4.16 kB
- SHA256:
- 898b5e30c607db955cfccd51274de18e2ce2240716ae49a96f046bdc1cc69643
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.