Instructions to use g8a9/roberta-tiny-2l-10M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use g8a9/roberta-tiny-2l-10M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="g8a9/roberta-tiny-2l-10M")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("g8a9/roberta-tiny-2l-10M") model = AutoModelForMaskedLM.from_pretrained("g8a9/roberta-tiny-2l-10M") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0376a6a3327cc164d5ad3dc942e3b72ed496bfe692d3bcc973deb6f8d0b14dbd
- Size of remote file:
- 3.5 kB
- SHA256:
- 37bc52937ba07a5f45a540cee72aa58f111f93b9cd64ac9a5bf4f4b37660f7be
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.