aasatorres/esm2-sae-topk-16384-k512
https://huggingface.co/aasatorres/esm2-sae-topk-16384-k512Sparse Autoencoder (SAE) trained on residue-level embeddings from ESM-2 (650M, layer 33) for interpretability research on protein language models.
Sourced from
- HuggingFace — aasatorres/esm2-sae-topk-16384-k512
Related resources
This model card provides an overview of the intended use of the ESMC SAE models and examples of how to access them, but it does not have a specific model or model weights. To access each SAE model collection, use the links below:
living-models/Botanic1-M-sae-k64-codebook8192
by living-modelsThis model card provides an overview of the intended use of the ESMC SAE models and examples of how to access them, but it does not have a specific model or model weights. To access each SAE model collection, use the links below:
This model card provides an overview of the intended use of the ESMC SAE models and examples of how to access them, but it does not have a specific model or model weights. To access each SAE model collection, use the links below:
AmelieSchreiber/esm_interact
by AmelieSchreiberThis model was finetuned on concatenated pairs of interacting proteins in much the same way as PepMLM. It is meant to generate interaction partners for proteins using the masked language modeling capabilities of ESM-2. The model is not well tested, so use with caution.
This model may be overfit to some extent (see below). Try running this notebook on the datasets linked to in the notebook. See if you can figure out why the metrics differ so much on the datasets. Is it due to something like sequence similarity in the train/test split?