Transformers
PyTorch
English
t5
text2text-generation
Trinidad and Tobago English Parser
Caribe
text-generation-inference
Instructions to use KES/T5-TTParser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KES/T5-TTParser with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KES/T5-TTParser") model = AutoModelForSeq2SeqLM.from_pretrained("KES/T5-TTParser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5187788e0c9fe96aa13ae1921a30010cecad83bf8c313033968a7ab6a691844f
- Size of remote file:
- 892 MB
- SHA256:
- 0dfb98850f328eb671fea1e0c4ca7a18ed0570121e05687c481d77fe6d7ee0ba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.