Instructions to use tals/roberta_python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tals/roberta_python with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tals/roberta_python")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tals/roberta_python") model = AutoModelForMaskedLM.from_pretrained("tals/roberta_python") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8736a51e13092548ef053f5266e41c658a7a58607e9e112e944a44d5cc72d637
- Size of remote file:
- 501 MB
- SHA256:
- 4d26c745ac740491f709243e33bf096a9e5f0baba02a40d215d027f666faac60
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.