from transformers import AutoTokenizer
from t5_vae_flax.src.t5_vae import FlaxT5VaeForAutoencoding
tokenizer = AutoTokenizer.from_pretrained("t5-base")
model = FlaxT5VaeForAutoencoding.from_pretrained("flax-community/t5-vae-wiki")
Setup
Run
setup_tpu_vm_venv.sh
to setup a virtual enviroment on a TPU VM for training.
Runs of flax-community t5-vae-wiki on huggingface.co
6
Total runs
0
24-hour runs
-1
3-day runs
-1
7-day runs
-10
30-day runs
More Information About t5-vae-wiki huggingface.co Model
t5-vae-wiki huggingface.co is an AI model on huggingface.co that provides t5-vae-wiki's model effect (), which can be used instantly with this flax-community t5-vae-wiki model. huggingface.co supports a free trial of the t5-vae-wiki model, and also provides paid use of the t5-vae-wiki. Support call t5-vae-wiki model through api, including Node.js, Python, http.
t5-vae-wiki huggingface.co is an online trial and call api platform, which integrates t5-vae-wiki's modeling effects, including api services, and provides a free online trial of t5-vae-wiki, you can try t5-vae-wiki online for free by clicking the link below.
flax-community t5-vae-wiki online free url in huggingface.co:
t5-vae-wiki is an open source model from GitHub that offers a free installation service, and any user can find t5-vae-wiki on GitHub to install. At the same time, huggingface.co provides the effect of t5-vae-wiki install, users can directly use t5-vae-wiki installed effect in huggingface.co for debugging and trial. It also supports api for free installation.