Instructions to use aparajitha/mt5-tr-sci-ms with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aparajitha/mt5-tr-sci-ms with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("aparajitha/mt5-tr-sci-ms") model = AutoModelForSeq2SeqLM.from_pretrained("aparajitha/mt5-tr-sci-ms", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from aparajitha/mt5-tr-sci-ms: direct link, hf CLI and curl.
- Browser
- Download file 16.4 MB
-
https://huggingface.co/aparajitha/mt5-tr-sci-ms/resolve/main/tokenizer.json
- Command line
-
hf download hf://aparajitha/mt5-tr-sci-ms/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/aparajitha/mt5-tr-sci-ms/resolve/main/tokenizer.json
16.4 MB
- Xet hash:
- 99e34e7a89ad33e6e251f50c491aba09bc461a20f64ca117d78b1eec4b21c70f
- Size of remote file:
- 16.4 MB
- SHA256:
- 8a043d44d33edbf6ab077036570a143722acb03bbf4bdeffb9340041173ba0b2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.