Our great sponsors
-
tape
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology. (by songlab-cal)
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
We evaluated based on downstream tasks (multiple supervised benchmarks, including 4 from TAPE), not the LM performance.
That should be trivial for it, attention models are good for "feature X exists somewhere in the text"/ That said, if your feature is just the presence of some short motif, why not just use n-gram/k-mer features? Those are invariant to location, and super fast/simple. I did some packages in the past for that, specially for proteins (PROFET, ASAP(for residue level)).
That should be trivial for it, attention models are good for "feature X exists somewhere in the text"/ That said, if your feature is just the presence of some short motif, why not just use n-gram/k-mer features? Those are invariant to location, and super fast/simple. I did some packages in the past for that, specially for proteins (PROFET, ASAP(for residue level)).
Related posts
- Logistic Regression for Image Classification Using OpenCV
- AI for AWS Documentation
- [D] Handling Concurrent Request for ML Model API
- Pre-Trained ML models for labeling retail images? Upload an image of a dress shirt and the labels output are “long sleeve, men’s, button down, collar, formal, dress shirt” or better?
- Meet Graphein: a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures and Interaction Networks