Instructions to use InstaDeepAI/IDP-ESM2-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InstaDeepAI/IDP-ESM2-150M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="InstaDeepAI/IDP-ESM2-150M")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/IDP-ESM2-150M") model = AutoModelForMaskedLM.from_pretrained("InstaDeepAI/IDP-ESM2-150M") - Notebooks
- Google Colab
- Kaggle
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# IDP-ESM2-8M
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**IDP-ESM2-150M** is an ESM2-style encoder for intrinsically disorded protein sequence representation learning, trained on [IDP-Euka-90](https://huggingface.co/datasets/
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This repository provides a Transformer encoder suitable for extracting **per-sequence embeddings** (mean-pooled over residues with padding masked out).
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# IDP-ESM2-8M
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**IDP-ESM2-150M** is an ESM2-style encoder for intrinsically disorded protein sequence representation learning, trained on [IDP-Euka-90](https://huggingface.co/datasets/InstaDeepAI/IDP-Euka-90).
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This repository provides a Transformer encoder suitable for extracting **per-sequence embeddings** (mean-pooled over residues with padding masked out).
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