Med-Doc-Classifier

Medical ASR model for Indian English, Hindi, Telugu, Kannada, Marathi.

Model for schema-driven extraction from Indian medical records: prescriptions, invoices, lab reports, and discharge summaries, plus PII detection.

Image PII redaction tuned for indian medical documents

Parrotlet-e is a state of the art multilingual medical entity embedding model designed for understanding and linking medical terms across Indian languages.

Purpose driven model for medical ASR in English.

Purpose driven medical records parsing model

A biomedical NLP model pre-trained from scratch on abstracts and full-text articles from PubMed and PubMed Central, achieving state-of-the-art performance on biomedical language understanding tasks.

A specialized biomedical language model pre-trained on radiology reports from MIMIC-CXR and PubMed, optimized for radiology NLP tasks such as masked language modeling, phrase grounding, and medical text classification.

A latent diffusion model for editing chest X-ray images by adding or removing abnormalities, designed for stress-testing biomedical vision models and evaluating their robustness in clinical AI research.

A domain-specific vision-language model designed for analyzing chest X-rays and radiology reports, leveraging temporal multi-modal pre-training for improved biomedical inference, phrase grounding, and image-text alignment.

A specialized biomedical language model trained on chest X-ray reports from MIMIC-CXR and PubMed, optimized for radiology NLP, masked language modeling, and multi-modal contrastive learning with image-text alignment.

A biomedical vision-language foundation model trained on PMC-15M, using PubMedBERT as the text encoder and Vision Transformer as the image encoder, optimized for cross-modal retrieval, classification, and visual question answering in medical AI applications.

A vision transformer model trained with self-supervised learning for encoding chest X-rays, optimized for medical image analysis, classification, retrieval, and report generation.

A multimodal Transformer model designed for generating grounded and non-grounded radiology reports from chest X-rays, integrating vision and language understanding for medical AI research.

A vision transformer model trained for chest X-ray encoding using self-supervised learning, optimized for medical image analysis and report generation.

A state-of-the-art EEG Brain Foundation Model.

A multimodal pre-trained Transformer model designed for multilingual document AI, integrating text, layout, and image data to improve visually rich document understanding across multiple languages.

An improved multimodal Transformer model for document AI, integrating text, layout, and image pre-training for enhanced document understanding tasks such as form recognition, document classification, and visual question answering.

A pre-trained multimodal Transformer model for document AI, integrating unified text and image masking for tasks such as form understanding, receipt processing, and document layout analysis.

This model card is for the Multi-Token Prediction (MTP) drafters for the Gemma 4 models. MTP is implemented by extending the base model with a smaller, faster draft model. When used in a Speculative Decoding pipeline, the draft model predicts several tokens ahead, which the target model then verifies in parallel. This results in significant decoding speedups (up to 3x) while guaranteeing the exact same quality as standard generation, making these checkpoints perfect for low-latency and ODML.

DiffusionGemma is a generative model built by Google DeepMind. Based on the 26B A4B Mixture-of-Experts (MoE) Gemma 4 architecture, DiffusionGemma generates tokens using discrete diffusion. This open-weights model is multimodal, handling text, image, and video inputs to generate text output.

Model for schema-driven extraction from Indian medical records: prescriptions, invoices, lab reports, and discharge summaries, plus PII detection.

Image PII redaction tuned for indian medical documents