Instructions to use Taykhoom/RNAErnie2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taykhoom/RNAErnie2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Taykhoom/RNAErnie2", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Taykhoom/RNAErnie2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -26,7 +26,7 @@ length <= 2048), and switches to an RNA-native vocabulary (U instead of T).
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| Intermediate size | 3072 |
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| Vocabulary size | 11 |
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| Positional encoding | Absolute learned |
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| Architecture |
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| Max sequence length | 2048 |
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**Vocabulary:** `[PAD]=0, [UNK]=1, [CLS]=2, [EOS]=3, [SEP]=4, [MASK]=5, A=6, U=7, C=8, G=9, N=10`
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| Intermediate size | 3072 |
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| Vocabulary size | 11 |
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| Positional encoding | Absolute learned |
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| Architecture | Post-LN BERT / BertForMaskedLM |
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| Max sequence length | 2048 |
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**Vocabulary:** `[PAD]=0, [UNK]=1, [CLS]=2, [EOS]=3, [SEP]=4, [MASK]=5, A=6, U=7, C=8, G=9, N=10`
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