Instructions to use Larxel/clin-deberta-br-gen-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Larxel/clin-deberta-br-gen-checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Larxel/clin-deberta-br-gen-checkpoints")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Larxel/clin-deberta-br-gen-checkpoints") model = AutoModelForMaskedLM.from_pretrained("Larxel/clin-deberta-br-gen-checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Larxel/clin-deberta-br-gen-checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 599 MB
-
https://huggingface.co/Larxel/clin-deberta-br-gen-checkpoints/resolve/main/model.safetensors
- Command line
-
hf download hf://Larxel/clin-deberta-br-gen-checkpoints/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Larxel/clin-deberta-br-gen-checkpoints/resolve/main/model.safetensors
599 MB
- Xet hash:
- 419bc689f7f28364140902b4fa04d1455c22c00dbce1972e36625ad4b9e64c38
- Size of remote file:
- 599 MB
- SHA256:
- 6704abba592283ebcd917ac374411f7ac2e7b6751272824aa9669bca543ccb84
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.