This model is a fine-tuned version of StanfordAIMI/RadLLaMA-7b. It achieves the following results on the evaluation set: - Loss: 0.0644 This model is finetuned to evaluate the difference between the reference and candidate radiology report for Chest Xrays. The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 32 - total_train_batch_size: 2048 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 3.0

Text generation

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RadBERT

StanfordAIMI

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Fill-mask

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RadLLaMA-7b

StanfordAIMI

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Text generation

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SRR-BERT-Leaves

StanfordAIMI

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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SRR-BERT-Leaves-with-Statuses

StanfordAIMI

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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stanford-deidentifier-base

StanfordAIMI

Stanford de-identifier was trained on a variety of radiology and biomedical documents with the goal of automatising the de-identification process while reaching satisfactory accuracy for use in production. Manuscript in-proceedings. These model weights are the recommended ones among all available deidentifier weights. This work was supported in part by the Medical Imaging and Data Resource Center (MIDRC), which is funded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under contract 75N92020D00021 and through The Advanced Research Projects Agency for Health (ARPA-H)

Text generationOpen weights

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stanford-deidentifier-v2

StanfordAIMI

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Text analysis

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stanford-deidentifier-with-radiology-reports-and-i2b2

StanfordAIMI

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Text analysis

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XraySigLIPvit-l-16-siglip-384webli

StanfordAIMI

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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