Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use devagonal/mt5-semantic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devagonal/mt5-semantic with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("devagonal/mt5-semantic") model = AutoModelForSeq2SeqLM.from_pretrained("devagonal/mt5-semantic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from devagonal/mt5-semantic: direct link, hf CLI and curl.
- Browser
- Download file 4.31 MB
-
https://huggingface.co/devagonal/mt5-semantic/resolve/main/spiece.model
- Command line
-
hf download hf://devagonal/mt5-semantic/spiece.model
-
curl -L -o spiece.model https://huggingface.co/devagonal/mt5-semantic/resolve/main/spiece.model
4.31 MB
- Xet hash:
- c002d43daac2cff8ed238836274d14498980e85c24cfd4076159b27131e91e77
- Size of remote file:
- 4.31 MB
- SHA256:
- ef78f86560d809067d12bac6c09f19a462cb3af3f54d2b8acbba26e1433125d6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.