Transformers
PyTorch
English
t5
text2text-generation
t5-small
natural language understanding
conversational system
task-oriented dialog
Eval Results (legacy)
text-generation-inference
Instructions to use ConvLab/t5-small-nlu-tm3-context3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ConvLab/t5-small-nlu-tm3-context3 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ConvLab/t5-small-nlu-tm3-context3") model = AutoModelForSeq2SeqLM.from_pretrained("ConvLab/t5-small-nlu-tm3-context3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b85785d61d899ee3ebebd476234d14e8b285daedd22109549b05846d40e8e26
- Size of remote file:
- 242 MB
- SHA256:
- bc7639bbb4e6a76262c5901569853d2abc2d6ce4945bf2682690af8e05db88e2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.