Instructions to use yangheng/PlantRNA-FM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yangheng/PlantRNA-FM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="yangheng/PlantRNA-FM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("yangheng/PlantRNA-FM") model = AutoModelForMaskedLM.from_pretrained("yangheng/PlantRNA-FM") - Notebooks
- Google Colab
- Kaggle
Interpretation
Hello Developer,
Please help me to interpret the following output using your model with the pipe.
Thanks
pipe =pipeline(model="yangheng/PlantRNA-FM")
pipe('GCCGACUUAGCUCAGUmaskGGGAGAGCGUUAGACUGAAGAUCUAAAGGUCCCUGGUUCGAUCCCGGGAGUCGGCACCA')
[{'score': 0.1147894561290741, 'token': 10, 'token_str': 'a', 'sequence': 'G C C G A C U U A G C U C A G U a G G G A G A G C G U U A G A C U G A A G A U C U A A A G G U C C C U G G U U C G A U C C C G G G A G U C G G C A C C A'}, {'score': 0.1147894561290741, 'token': 4, 'token_str': 'A', 'sequence': 'G C C G A C U U A G C U C A G U A G G G A G A G C G U U A G A C U G A A G A U C U A A A G G U C C C U G G U U C G A U C C C G G G A G U C G G C A C C A'}, {'score': 0.1139269545674324, 'token': 5, 'token_str': 'C', 'sequence': 'G C C G A C U U A G C U C A G U C G G G A G A G C G U U A G A C U G A A G A U C U A A A G G U C C C U G G U U C G A U C C C G G G A G U C G G C A C C A'}, {'score': 0.1139269545674324, 'token': 11, 'token_str': 'c', 'sequence': 'G C C G A C U U A G C U C A G U c G G G A G A G C G U U A G A C U G A A G A U C U A A A G G U C C C U G G U U C G A U C C C G G G A G U C G G C A C C A'}, {'score': 0.09095487743616104, 'token': 12, 'token_str': 'g', 'sequence': 'G C C G A C U U A G C U C A G U g G G G A G A G C G U U A G A C U G A A G A U C U A A A G G U C C C U G G U U C G A U C C C G G G A G U C G G C A C C A'}]