andrewdalpino commited on
Commit
78f6a4e
·
verified ·
1 Parent(s): a4666ee

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +3 -3
README.md CHANGED
@@ -5,15 +5,15 @@ datasets:
5
  tags:
6
  - esmc
7
  ---
8
- # ESM ProtHash
9
 
10
- A protein language model that outputs amino acid sequence embeddings for use in clustering, classification, locality-sensitive hashing, and more. Distilled from the [ESMC](https://www.evolutionaryscale.ai/blog/esm-cambrian) family of models with deep comprehension of protein structure, ProtHash produces contextual embeddings that align in vector space according to the sequences' atomic structure. Trained on the [SwissProt](https://huggingface.co/datasets/andrewdalpino/SwissProt-Gene-Ontology) dataset to mimic the activations of its ESMC teacher model, ProtHash embeddings have near-perfect similarity to ESMC embeddings but at a greatly reduced computational cost.
11
 
12
  ## Key Features
13
 
14
  - **Blazing fast and efficient**: ProtHash uses as few as 1.5% of its ESMC teacher's total parameters to achieve near-perfect cosine similarity between the two embedding spaces.
15
 
16
- - **Structurally-relevant**: Structurally similar proteins will show up nearby in the embedding space enabling downstream tasks such as clustering, classification, and locality-sensitive hashing based on atomic structure.
17
 
18
  - **Compatible with ESMC**: ProtHash can output embeddings in its native or ESMC teacher's dimensionality - allowing it to serve as either a faster drop-in approximation to ESMC embeddings or a more efficient compressed representation.
19
 
 
5
  tags:
6
  - esmc
7
  ---
8
+ # ProtHash
9
 
10
+ A protein language model that outputs amino acid sequence embeddings for use in clustering, classification, locality-sensitive hashing, and more. Distilled from the [ESMC](https://www.evolutionaryscale.ai/blog/esm-cambrian) family of models, ProtHash produces contextual embeddings that align in vector space according to the sequences' underlying biological properties such as structure and function. Trained on the [SwissProt](https://huggingface.co/datasets/andrewdalpino/SwissProt-Gene-Ontology) dataset to mimic the activations of its ESMC teacher model, ProtHash embeddings have near-perfect similarity to ESMC embeddings but at a greatly reduced computational cost.
11
 
12
  ## Key Features
13
 
14
  - **Blazing fast and efficient**: ProtHash uses as few as 1.5% of its ESMC teacher's total parameters to achieve near-perfect cosine similarity between the two embedding spaces.
15
 
16
+ - **Biologically-relevant**: Biologically similar proteins will show up nearby in the embedding space enabling downstream tasks such as clustering, classification, and locality-sensitive hashing based on atomic structure.
17
 
18
  - **Compatible with ESMC**: ProtHash can output embeddings in its native or ESMC teacher's dimensionality - allowing it to serve as either a faster drop-in approximation to ESMC embeddings or a more efficient compressed representation.
19