Instructions to use kd13/RoPERT-MLM-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kd13/RoPERT-MLM-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kd13/RoPERT-MLM-small", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("kd13/RoPERT-MLM-small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cc381fa6d36ceb640342a3d5fc3529fc2898d84f860674d34c5d25e7166c0960
- Size of remote file:
- 170 MB
- SHA256:
- f6d32123898520b79f57ee7d1e84ba117683332683530fe619c80ee30660d61c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.