Instructions to use plncmm/mdeberta-wl-base-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use plncmm/mdeberta-wl-base-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="plncmm/mdeberta-wl-base-es")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("plncmm/mdeberta-wl-base-es") model = AutoModelForMaskedLM.from_pretrained("plncmm/mdeberta-wl-base-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from plncmm/mdeberta-wl-base-es: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/plncmm/mdeberta-wl-base-es/resolve/main/training_args.bin
- Command line
-
hf download hf://plncmm/mdeberta-wl-base-es/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/plncmm/mdeberta-wl-base-es/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 9ca4cb81d2dd7bd7bfe6d1d18f1218e356045c1b421e7288b7178e4038be0b1e
- Size of remote file:
- 3.31 kB
- SHA256:
- 4cd05523275a552b097227da22fea8b685f7315829e8eb9245854d6202afde81
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.