Instructions to use tharindu/roberta-25 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tharindu/roberta-25 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tharindu/roberta-25")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tharindu/roberta-25") model = AutoModelForMaskedLM.from_pretrained("tharindu/roberta-25") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c128dc17bd12297e0b2479ee5eeb36d143915ffb3254fa9898e3c0a897b994b
- Size of remote file:
- 3.44 kB
- SHA256:
- 02782e2c3e72f2aa97c31601cc7c6f52e76d802367b2717a21a5174b943b2e9a
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