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EndoViT

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This model is a fine-tuned version of DeBERTa on the PubMED Dataset.

Using llama.cpp release b2440 for quantization.

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## 💡 Model description This repo contains a large molecular generative model built with molecular language SELFIES.

Using llama.cpp commit fa97464 for quantization.

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# Mr-Grammatology-clinical-problems-Mistral-7B-0.5 !image/png

Abstract:

This is a merge of pre-trained language models created using mergekit.

Abstract:

This is a merge of pre-trained language models created using mergekit.

This is a merge of pre-trained language models created using mergekit.

This modelcard aims to be a base template for new models. It has been generated using this raw template.

SMILES2IUPAC-canonical-base was designed to accurately translate SMILES chemical names to IUPAC standards.

### Model Description A machine learning model for waste classification

# TinyLlama-1.1B

The T5 Large for Medical Text Summarization is a specialized variant of the T5 transformer model, fine-tuned for the task of summarizing medical text. This model is designed to generate concise and coherent summaries of medical documents, research papers, clinical notes, and other…

This is official model checkpoint for Asclepius-13B (arxiv). This model is the first publicly shareable clinical LLM, trained with synthetic data.

Google ViT model is finetuned on lung and colon histopathology image classification dataset. The dataset is available on Kaggle.

Details coming soon

# Meditron 70B - GGUF - Model creator: EPFL LLM Team - Original model: Meditron 70B

A Vision Transformer (ViT) image classification model. \ Trained on 15M histology patches from PAIP and TCGA. \ Used the MoCo v3 self supervised learning method.

This is Vision Transformer model trained for cancer classification. To make single model to predict any cancer, I trained this ViT model. following are the cancer types that model can predict: Brain cancer Breast Cancer (histopathology) Lung & Colon Cancer (histopathology) Cervical Caner Kidney…

This 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.

ProstT5 is a protein language model (pLM) which can translate between protein sequence and structure. !ProstT5 pre-training and inference

A Vision Transformer (ViT) image classification model. \ Trained by Owkin on 40 million pan-cancer histology tiles from TCGA-COAD.

A Vision Transformer (ViT) image classification model. \ Trained by Owkin on 40M pan-cancer histology tiles from TCGA. \ Fine-tuned on LC25000's lung subset.

## Model Description The "Bird Species Classifier" is a state-of-the-art image classification model designed to identify various bird species from images. It uses the EfficientNet architecture and has been fine-tuned to achieve high accuracy in recognizing a wide range of bird species.

A Vision Transformer (ViT) image classification model. \ Trained on 2M histology patches from TCGA-BRCA.

QuiltNet-B-32 is a CLIP ViT-B/32 vision-language foundation model trained on the Quilt-1M dataset curated from representative histopathology videos. It can perform various vision-language processing (VLP) tasks such as cross-modal retrieval, image classification, and visual question answering.

This is a medicine-focussed mistral fine tuned using keivalya/MedQuad-MedicalQnADataset

Question Answering Model for the PathoTHREAT Project

Pre-trained weights and exported models for our spine segmentation project. The source code, designed to reproduce our test results and facilitate training and running inference on your own data, is available on GitHub: https://github.com/MMIV-ML/fastMONAI/tree/master/research

MentaLLaMA-chat-7B is part of the MentaLLaMA project, the first open-source large language model (LLM) series for interpretable mental health analysis with instruction-following capability. This model is finetuned based on the Meta LLaMA2-chat-7B foundation model and the full IMHI instruction…

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?

+ Model Name: Med_English2Spanish + Model Type: Transformer-based Neural Machine Translation (NMT) Model + Task: English to Spanish Medical Translation

This model is a fine-tuned model based on the Llama 2_7b architecture. It has been specifically trained on a dataset comprising USMLE (United States Medical Licensing Examination) questions and answers, as well as conversations between doctors and patients.

项目地址：https://github.com/iioSnail/chinesemedicalner

This is a Japanese RoBERTa base model pre-trained on academic articles in medical sciences collected by Japan Science and Technology Agency (JST).

I present a demo showcasing retinal vessel segmentation using the U-Net model, which is a well-known and widely used model in medical image segmentation. The model was trained on the DRIVE dataset, and the training process was conducted on Google Colab.

datasets: - UMLS

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.

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.