CoolFace
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alexandroputra/medsiglip-448-ft-tb-screening

sourceHugging Faceotherupdated 1y agoView on Hugging Face
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Model Card

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medsiglip-448-ft-tb-screening

This model is a fine-tuned version of google/medsiglip-448 on an unknown dataset. It achieves the following results on the evaluation set:

  • —Loss: 2.6822

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • —learning_rate: 5e-05
  • —trainbatchsize: 4
  • —evalbatchsize: 4
  • —seed: 42
  • —gradientaccumulationsteps: 16
  • —totaltrainbatch_size: 64
  • —optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • —lrschedulertype: cosine
  • —lrschedulerwarmup_steps: 10
  • —num_epochs: 10

Training results

Training LossEpochStepValidation Loss
3.50640.2140252.4667
1.94060.4280502.5449
1.91750.6421752.5669
1.86590.85611002.7958
1.96031.06851252.6281
1.88111.28251502.5601
1.89551.49651752.5833
1.89821.71052002.6373
1.8251.92462252.6426
1.882.13702502.8641
1.8512.35102752.6415
1.86192.56503002.5749
1.83652.77903252.6245
1.87832.99303502.5929
1.86933.20553752.5986
1.86053.41954002.6601
1.87593.63354252.5904
1.87313.84754502.6054
1.85364.05994752.6441
1.85094.27395002.6678
1.86094.48805252.6946
1.84784.70205502.6386
1.84924.91605752.6799
1.85495.12846002.6355
1.885.34246252.7021
1.85695.55646502.6380
1.8625.77056752.6349
1.84865.98457002.6843
1.85036.19697252.6926
1.85036.41097502.6962
1.846.62497752.6286
1.84666.83908002.6278
1.85847.05148252.6274
1.86337.26548502.6308
1.87447.47948752.6365
1.85227.69349002.6514
1.85787.90749252.6701
1.86618.11999502.6817
1.83018.33399752.6813
1.84998.547910002.6841
1.84848.761910252.6832
1.88158.975910502.6814
1.80829.188310752.6836
1.83029.402411002.6839
1.88229.616411252.6824
1.86489.830411502.6822

Framework versions

  • —Transformers 4.56.0
  • —Pytorch 2.8.0+cu128
  • —Datasets 4.0.0
  • —Tokenizers 0.22.0