Instructions to use tharindu/roberta-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tharindu/roberta-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tharindu/roberta-50")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tharindu/roberta-50") model = AutoModelForMaskedLM.from_pretrained("tharindu/roberta-50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0df5d0fd40995d586f5967981ad59792195f45404e2e31114ee9dcbca094b67d
- Size of remote file:
- 2.84 GB
- SHA256:
- 36e6068c7cc66d29a860c30acd47d01ff91fd30359b7edc7450e162efcde9f56
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