IDP-ESM2-8M

IDP-ESM2-8M is an ESM2-style encoder for intrinsically disorded protein sequence representation learning, trained on IDP-Euka-90.
This repository provides a Transformer encoder suitable for extracting per-sequence embeddings (mean-pooled over residues with padding masked out).

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Quick start: generate mean-pooled embeddings

The snippet below loads the tokenizer and model, runs a forward pass on a couple of sequences, and computes mean-pooled embeddings (ignoring padding) — exactly the setup typically used for downstream tasks.

from transformers import AutoTokenizer, AutoModel
import torch

# --- Config ---
model_name = "InstaDeepAI/IDP-ESM2-8M"

# --- Load model and tokenizer ---
tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D")
model = AutoModel.from_pretrained(model_name)
model.eval()

# (optional) use GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# --- Input sequences ---
sequences = [
    "MDDNHYPHHHHNHHNHHSTSGGCGESQFTTKLSVNTFARTHPMIQNDLIDLDLISGSAFTMKSKSQQ",
    "PADRDLSSPFGSTVPGVGPNAAAASNAAAAAAAAATAGSNKHQTPPTTFR",
]

# --- Tokenize ---
inputs = tokenizer(
    sequences,
    return_tensors="pt",
    padding=True,
    truncation=True,
)
inputs = {k: v.to(device) for k, v in inputs.items()}

# --- Forward pass ---
with torch.no_grad():
    outputs = model(**inputs)
    embeddings = outputs.last_hidden_state  # shape: (batch, seq_len, hidden_dim)