Instructions to use vachevkd/dg-t5base-race with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vachevkd/dg-t5base-race with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vachevkd/dg-t5base-race") model = AutoModelForSeq2SeqLM.from_pretrained("vachevkd/dg-t5base-race", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d9553d68e29d4d2d519290a0df283f1ac59e2a4a5960c468140b02ceff9f4c9
- Size of remote file:
- 892 MB
- SHA256:
- 5ccdbf4415c6002557bcc2bd227d3dc9d7d5864b8b4fd2f1429650eb59da647b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.