Instructions to use syssec-utd/py315-pylingual-v3-mlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use syssec-utd/py315-pylingual-v3-mlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="syssec-utd/py315-pylingual-v3-mlm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("syssec-utd/py315-pylingual-v3-mlm") model = AutoModelForMaskedLM.from_pretrained("syssec-utd/py315-pylingual-v3-mlm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6c41cc45c9e03f127db50e96a5c2c2035150fb494f7afb2cb87c4320f180e4f
- Size of remote file:
- 5.27 kB
- SHA256:
- 880e6307de0c60f7307e489b1f996d821733fa1d82be146cfaa5ecd2ba7cc1aa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.