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
Upload 2 files
Browse files- README.md +53 -3
- gitattributes +35 -0
README.md
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---
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library_name: transformers
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pipeline_tag: feature-extraction
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model_name: InstaDeepAI/IDP-ESM2-8M
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---
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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/jeanq1/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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---
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## Quick start: generate embeddings
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The snippet below loads the tokenizer and model, runs a forward pass on a couple of sequences and extracts embeddings for each sequence.
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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# --- Config ---
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model_name = "InstaDeepAI/IDP-ESM2-150M"
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# --- Load model and tokenizer ---
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tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D")
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model = AutoModel.from_pretrained(model_name)
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model.eval()
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# (optional) use GPU if available
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model.to(device)
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# --- Input sequences ---
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sequences = [
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"MDDNHYPHHHHNHHNHHSTSGGCGESQFTTKLSVNTFARTHPMIQNDLIDLDLISGSAFTMKSKSQQ",
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"PADRDLSSPFGSTVPGVGPNAAAASNAAAAAAAAATAGSNKHQTPPTTFR",
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]
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# --- Tokenize ---
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inputs = tokenizer(
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sequences,
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return_tensors="pt",
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padding=True,
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truncation=True,
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)
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inputs = {k: v.to(device) for k, v in inputs.items()}
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# --- Forward pass ---
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with torch.no_grad():
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outputs = model(**inputs)
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embeddings = outputs.last_hidden_state # shape: (batch, seq_len, hidden_dim)
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