Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use SnehaPriyaaMP/html-accessibility-fixer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SnehaPriyaaMP/html-accessibility-fixer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("SnehaPriyaaMP/html-accessibility-fixer") model = AutoModelForSeq2SeqLM.from_pretrained("SnehaPriyaaMP/html-accessibility-fixer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from SnehaPriyaaMP/html-accessibility-fixer: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/SnehaPriyaaMP/html-accessibility-fixer/resolve/main/training_args.bin
- Command line
-
hf download hf://SnehaPriyaaMP/html-accessibility-fixer/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/SnehaPriyaaMP/html-accessibility-fixer/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- f3d9ff0b584a0b8401d9a7eb6fe4e24ca48c949b69fd94af8cb099621b6aa6e0
- Size of remote file:
- 5.3 kB
- SHA256:
- ca293ebda97395e28f8cadb761a20ccfc17f49e0640e91e4942a6406e654ff06
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.