Models
This model was previously named "PubMedBERT (abstracts + full text)".
This is the pretrained model presented in SciBERT: A Pretrained Language Model for Scientific Text, which is a BERT model trained on scientific
fill-mask artifact mirrored from the HuggingFace Hub.
The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (casedL-12H-768A-12) or BioBERT…
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language…
Note: This model was previously known as PubMedGPT 2.7B, but we have changed it due to a request from the NIH which holds the trademark for
CXR-BERT is a chest X-ray (CXR) domain-specific language model that makes use of an improved vocabulary, novel pretraining procedure, weight regularization,…
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective.
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective.
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective.
Pretrained model on protein sequences using a masked language modeling (MLM) objective.
fill-mask artifact mirrored from the HuggingFace Hub.
ChemBERTa: Training a BERT-like transformer model for masked language modelling of chemical SMILES
About the Model An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus…
text-generation artifact mirrored from the HuggingFace Hub.
Model description - Intended uses and limitations - How to use - Training - Evaluation - Additional information - Author - Licensing information - Citation…
Model description - Intended uses and limitations - How to use - Limitations and bias - Training - Evaluation - Additional information - Author - Contact…
A Named Entity Recognition model for clinical entities (problem, treatment,
This model card describes the ClinicalBERT model, which was trained on a large multicenter dataset with a large corpus of 1.2B words of diverse diseases we…
The Clinical Assertion and Negation Classification BERT is introduced in the paper Assertion Detection in Clinical Notes: Medical Language Models to the…
SapBERT-PubMedBERT SapBERT by Liu et al.
This is a PubMedBERT-base model fined-tuned using sentence-transformers.
BiomedCLIP is a biomedical vision-language foundation model that is pretrained on PMC-15M, a dataset of 15 million figure-caption pairs extracted from…
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or…
An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO).
ESM Protein Localization Model Model description The ESM Protein Localization Model is a deep learning model trained on protein sequences to predict their…
An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO).
An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO).
This repository contains 356 Pfam-level protein language model checkpoints generated for the study “Does domain-specific unsupervised fine-tuning improve…
This repository contains 254 Pfam-level protein language model checkpoints generated for the study “Does domain-specific unsupervised fine-tuning improve…
This repository contains 171 Clan-level protein language model checkpoints generated for the study “Does domain-specific unsupervised fine-tuning improve…
This repository contains 82 Clan-level protein language model checkpoints generated for the study “Does domain-specific unsupervised fine-tuning improve…
Model artifact mirrored from the HuggingFace Hub.
Model Card for ChemBERTa-10M-MTR Model Details Model Description More information needed - Developed by: DeepChem - Shared by [Optional]:
Model artifact mirrored from the HuggingFace Hub.
This repository contains a fine-tuned Vision Transformer (ViT) model for classifying chest X-ray images, utilizing the CheXpert dataset.
This model performs both segmentation and classification on chest radiographs (X-rays).
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Model artifact mirrored from the HuggingFace Hub.
Model artifact mirrored from the HuggingFace Hub.
SMAFormer: Synergistic Multi-Attention Transformer for Medical Image
Model artifact mirrored from the HuggingFace Hub.
Model description - Intended uses and limitations - How to use - Limitations and bias - Training - Tokenization and model pretraining - Training corpora and…
An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO).
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective.
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective.
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective.
Pretrained model on protein sequences using a masked language modeling (MLM) objective.
An encoder-only, half-precision version of the ProtT5-XL-UniRef50 model.
AMPLIFY is an efficient, state-of-the-art protein language model pre-trained using masked language modeling on UniRef100, OAS, and SCOP (UR100P).
AMPLIFY is an efficient, state-of-the-art protein language model pre-trained using masked language modeling on UniRef100, OAS, and SCOP (UR100P).
We have released ProtGPT3!
Mirror of the base ProGen2-small model (with slightly modified configuration and forward pass) by Nijkamp, et
Mirror of the base ProGen2-base model (with slightly modified configuration and forward pass) by Nijkamp, et
This set of model weights was released with the GitHub-compatible esm package format.
esm3-sm-open-v1 is trained on 2.78 billion natural proteins.
ESMFold is a state-of-the-art end-to-end protein folding model based on an ESM-2 backbone.
Boltz-1 is an open-source model which predicts the 3D structure of proteins, rna, dna and small molecules; it handles modified residues, covalent ligands and…
Model artifact mirrored from the HuggingFace Hub.
Chai-1 is a multi-modal foundation model for molecular structure prediction that performs at the state-of-the-art across a variety of benchmarks.
Model artifact mirrored from the HuggingFace Hub.
The Nucleotide Transformers are a collection of foundational language models that were pre-trained on DNA sequences from whole-genomes.
The Nucleotide Transformers are a collection of foundational language models that were pre-trained on DNA sequences from whole-genomes.
The Nucleotide Transformers are a collection of foundational language models that were pre-trained on DNA sequences from whole-genomes.
This is the official pre-trained model introduced in DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species
Welcome!
Using Caduceus To use the pre-trained model for masked language modeling, use the following snippet: python from transformers import AutoModelForMaskedLM,
Using Caduceus To use the pre-trained model for masked language modeling, use the following snippet: python from transformers import AutoModelForMaskedLM,
Evo 2 is a state-of-the-art DNA language model trained autoregressively on trillions of DNA
Evo 2 is a state-of-the-art DNA language model trained autoregressively on trillions of DNA
Evo 2 is a state-of-the-art DNA language model trained autoregressively on trillions of DNA
Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM)
Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM)
Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM)
Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM)
The code for using the CodonFM model checkpoint is available in the official Github
The code for using the CodonFM model checkpoint is available in the official Github
Geneformer Geneformer is a foundational transformer model pretrained on a large-scale corpus of human single cell transcriptomes to enable context-aware…
State Embedding model checkpoints are meant to be used with the Arc State
MoLFormer is a class of models pretrained on SMILES string representations of up to 1.1B molecules from ZINC and PubChem.
Channel-agnostic image encoding model CA-MAE with a ViT-S/16 encoder backbone designed for microscopy image featurization.
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
Code for the ProteinMPNN paper
Source code repository mirrored from GitHub (dauparas/LigandMPNN).
Code for running RFdiffusion
A deep learning-based tool to identify splice variants
Source code repository mirrored from GitHub (bowang-lab/scGPT).
Source code repository mirrored from GitHub (biomap-research/scFoundation).
UCE is a zero-shot foundation model for single-cell gene expression data
A unifying representation of single cell expression profiles that quantifies similarity between expression states and generalizes to represent new studies…
Deep probabilistic analysis of single-cell and spatial omics data
State is a machine learning model that predicts cellular perturbation response across diverse contexts
GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations
The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level.
Single cell perturbation prediction
Official Repository for the Uni-Mol Series Methods
Message Passing Neural Networks for Molecule Property Prediction
Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking