Dr-BERT/DrBERT-4GB

https://huggingface.co/Dr-BERT/DrBERT-4GB
Staleby Dr-BERT921updated 3 years ago
Python

In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains.

Sourced from

  • HuggingFaceDr-BERT/DrBERT-4GB

Related resources

In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains.

Stale1.5K3 years ago
Python

In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains.

Stale433 years ago
Python

🤗 Blog | 📄 Paper | 💻 Code | 🌐 FineMed | 🩺 DoctoBERT

Active4601 week ago
Python

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 constructed. We then utilized a large-scale corpus of EHRs from over 3 million patient records to fine tune the base language model.

Idle21.2K1 year ago
Python

This model is a lightweight model pre-trained on SELFIES (Self-Referencing Embedded Strings) representations of molecules. It is trained on 2.7M unique and valid molecules taken from COCONUTDB and ChemBL34, with 7.3M total generated masked examples.

Idle19 months ago
Python