Instructions to use yarongef/DistilProtBert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yarongef/DistilProtBert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="yarongef/DistilProtBert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("yarongef/DistilProtBert") model = AutoModelForMaskedLM.from_pretrained("yarongef/DistilProtBert") - Notebooks
- Google Colab
- Kaggle
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README.md
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DistilProtBert was pretrained on millions of proteins sequences.
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Few important differences between DistilProtBert model and the original ProtBert version are:
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## Intended uses & limitations
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DistilProtBert was pretrained on millions of proteins sequences.
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Few important differences between DistilProtBert model and the original ProtBert version are:
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1. Size of the model
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2. Size of the pretraining dataset
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3. Hardware used for pretraining
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## Intended uses & limitations
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