Transformers
PyTorch
TensorFlow
Safetensors
English
bart
text2text-generation
NatSight-AdpSeq2Seq
Text2SQL
Instructions to use C5i/NatSight-bart-base-wikisql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use C5i/NatSight-bart-base-wikisql with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("C5i/NatSight-bart-base-wikisql") model = AutoModelForSeq2SeqLM.from_pretrained("C5i/NatSight-bart-base-wikisql", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from C5i/NatSight-bart-base-wikisql: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/C5i/NatSight-bart-base-wikisql/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://C5i/NatSight-bart-base-wikisql/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/C5i/NatSight-bart-base-wikisql/resolve/main/pytorch_model.bin
558 MB
- Xet hash:
- 03a517c0195442b05d0d5046c10b4a488457fe5785dd3ee68f4ac663e334141f
- Size of remote file:
- 558 MB
- SHA256:
- c8c12a397944c05d12550a081df4f0c9818eca7e04239ea829cd7ba373b79aaa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.