Instructions to use sumanthbhargava/mt5-base-encoding-correction-1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sumanthbhargava/mt5-base-encoding-correction-1k with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sumanthbhargava/mt5-base-encoding-correction-1k") model = AutoModelForSeq2SeqLM.from_pretrained("sumanthbhargava/mt5-base-encoding-correction-1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 133 Bytes
4144fa3 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:1358dd3ec3b848b8701eca8d995d7181f6b23728277d79a8944f30ba13185cab
size 16330639
|