florence_ft / README.md
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metadata
library_name: transformers
base_model: HuggingFaceM4/Florence-2-DocVQA
tags:
  - generated_from_trainer
model-index:
  - name: florence_ft
    results: []

florence_ft

This model is a fine-tuned version of HuggingFaceM4/Florence-2-DocVQA on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0833

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-06
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss
4.4629 0.0123 25 4.6140
4.0165 0.0245 50 3.9075
3.0887 0.0368 75 2.4186
1.3752 0.0491 100 1.4240
1.1205 0.0613 125 1.2705
1.0809 0.0736 150 1.2144
1.0946 0.0859 175 1.1813
1.0311 0.0982 200 1.1653
1.0611 0.1104 225 1.1503
1.0209 0.1227 250 1.1423
1.052 0.1350 275 1.1384
1.0129 0.1472 300 1.1273
0.9764 0.1595 325 1.1218
0.9707 0.1718 350 1.1155
1.0024 0.1840 375 1.1151
1.0446 0.1963 400 1.1112
0.9691 0.2086 425 1.1081
1.0018 0.2209 450 1.1040
0.9806 0.2331 475 1.0989
1.0555 0.2454 500 1.0980
0.9778 0.2577 525 1.0981
0.988 0.2699 550 1.0962
0.988 0.2822 575 1.0939
0.9572 0.2945 600 1.0969
0.9802 0.3067 625 1.0952
0.9504 0.3190 650 1.0933
1.0194 0.3313 675 1.0948
0.9697 0.3436 700 1.0935
0.96 0.3558 725 1.0903
0.9665 0.3681 750 1.0924
0.9895 0.3804 775 1.0920
1.004 0.3926 800 1.0914
1.0054 0.4049 825 1.0909
0.9514 0.4172 850 1.0890
0.9996 0.4294 875 1.0906
0.99 0.4417 900 1.0896
0.9427 0.4540 925 1.0887
1.0014 0.4663 950 1.0883
0.9639 0.4785 975 1.0864
1.0073 0.4908 1000 1.0877
0.9895 0.5031 1025 1.0863
0.9594 0.5153 1050 1.0841
0.9559 0.5276 1075 1.0849
1.0034 0.5399 1100 1.0849
0.9795 0.5521 1125 1.0844
0.9661 0.5644 1150 1.0834
0.9533 0.5767 1175 1.0830
0.976 0.5890 1200 1.0830
0.9932 0.6012 1225 1.0846
1.0067 0.6135 1250 1.0861
0.9543 0.6258 1275 1.0854
0.9733 0.6380 1300 1.0844
0.9673 0.6503 1325 1.0837
0.9378 0.6626 1350 1.0837
0.9713 0.6748 1375 1.0840
0.9913 0.6871 1400 1.0838
0.9302 0.6994 1425 1.0837
0.9873 0.7117 1450 1.0836
0.9618 0.7239 1475 1.0835
1.0042 0.7362 1500 1.0835
0.9627 0.7485 1525 1.0827
0.9635 0.7607 1550 1.0827
0.9658 0.7730 1575 1.0828
0.9446 0.7853 1600 1.0832
0.9844 0.7975 1625 1.0833
0.9641 0.8098 1650 1.0837
1.0 0.8221 1675 1.0835
0.9514 0.8344 1700 1.0837
1.0094 0.8466 1725 1.0835
0.9379 0.8589 1750 1.0834
0.9617 0.8712 1775 1.0835
0.9674 0.8834 1800 1.0836
0.9867 0.8957 1825 1.0838
0.9442 0.9080 1850 1.0832
0.9603 0.9202 1875 1.0838
0.9766 0.9325 1900 1.0833
0.9806 0.9448 1925 1.0835
0.9676 0.9571 1950 1.0835
0.9856 0.9693 1975 1.0838
0.9339 0.9816 2000 1.0836
0.9553 0.9939 2025 1.0833

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.0.1+cu117
  • Tokenizers 0.19.1