Transformers
PyTorch
Chinese
mt5
text2text-generation
mt5-small
natural language understanding
conversational system
task-oriented dialog
Eval Results (legacy)
Instructions to use ConvLab/mt5-small-nlu-all-crosswoz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ConvLab/mt5-small-nlu-all-crosswoz with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ConvLab/mt5-small-nlu-all-crosswoz") model = AutoModelForSeq2SeqLM.from_pretrained("ConvLab/mt5-small-nlu-all-crosswoz", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 39e4f9a5e90d0b1bf86629670cc6692bd768254a66ab4ff62bb14de802cc6bfb
- Size of remote file:
- 1.2 GB
- SHA256:
- cbefa595cd4962f0189f78485a4eb437f7d0fcfd0bc4306b3fedf0421e4a7469
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.