Instructions to use Jasim9085/bart-autoencoder-thought-vector-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jasim9085/bart-autoencoder-thought-vector-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Jasim9085/bart-autoencoder-thought-vector-v2") model = AutoModelForSeq2SeqLM.from_pretrained("Jasim9085/bart-autoencoder-thought-vector-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a42bb7fba04eb14ec86233e7b1ea104b60510ad269e2fd5b23f48993799a5d3
- Size of remote file:
- 5.43 kB
- SHA256:
- a68ce2db4af22ab973d61473cc0b7f0c082129187edacbbf13ef0a800e1861b6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.