Instructions to use sumanthbhargava/mt5-base-encoding-correction-10k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sumanthbhargava/mt5-base-encoding-correction-10k with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sumanthbhargava/mt5-base-encoding-correction-10k") model = AutoModelForSeq2SeqLM.from_pretrained("sumanthbhargava/mt5-base-encoding-correction-10k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from sumanthbhargava/mt5-base-encoding-correction-10k: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/sumanthbhargava/mt5-base-encoding-correction-10k/resolve/main/tokenizer.json
- Command line
-
hf download hf://sumanthbhargava/mt5-base-encoding-correction-10k/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/sumanthbhargava/mt5-base-encoding-correction-10k/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 85160e850e816512aaee749c838b5f8f608a823756cb3b75f7229f3477463ab0
- Size of remote file:
- 16.3 MB
- SHA256:
- 1358dd3ec3b848b8701eca8d995d7181f6b23728277d79a8944f30ba13185cab
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