Instructions to use ChatterjeeLab/PepMLM-650M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChatterjeeLab/PepMLM-650M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ChatterjeeLab/PepMLM-650M")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ChatterjeeLab/PepMLM-650M") model = AutoModelForMaskedLM.from_pretrained("ChatterjeeLab/PepMLM-650M", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 125 Bytes
843af80 | 1 2 3 4 5 6 7 8 | {
"cls_token": "<cls>",
"eos_token": "<eos>",
"mask_token": "<mask>",
"pad_token": "<pad>",
"unk_token": "<unk>"
}
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