CoolFace
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TeeA/ViMATCHA

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face
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Model Card

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ViMATCHA

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

  • —Loss: 0.5748

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
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: linear
  • —num_epochs: 10

Training results

Training LossEpochStepValidation Loss
3.90770.16031503.3649
3.19260.21372002.7038
2.55650.26712502.1718
2.15520.32053001.7380
1.78340.37393501.4287
1.4280.42744001.2302
1.38670.48084501.1280
1.14960.53425001.0437
1.24140.58765500.9901
1.1160.64106000.9475
1.120.69446500.9073
1.01320.74797000.8859
0.99540.80137500.8638
1.00570.85478000.8496
0.99290.90818500.8289
0.9450.96159000.8207
0.8661.01509500.8059
0.89011.068410000.7944
0.83821.121810500.7828
0.9461.175211000.7748
0.90421.228611500.7662
0.83341.282112000.7549
0.87471.335512500.7501
0.82241.388913000.7424
0.79981.442313500.7382
0.90221.495714000.7341
0.82971.549114500.7226
0.80141.602615000.7165
0.84231.656015500.7129
0.72861.709416000.7063
0.73611.762816500.7040
0.82031.816217000.6982
0.81031.869717500.6945
0.72511.923118000.6926
0.71931.976518500.6910
0.81332.029919000.6843
0.75452.083319500.6862
0.80252.136820000.6768
0.74212.190220500.6769
0.68992.243621000.6744
0.76072.297021500.6690
0.7392.350422000.6652
0.70952.403822500.6666
0.73922.457323000.6605
0.73072.510723500.6560
0.6852.564124000.6579
0.64192.617524500.6499
0.68942.670925000.6532
0.62882.724425500.6482
0.70242.777826000.6471
0.77172.831226500.6475
0.73892.884627000.6434
0.69442.938027500.6406
0.65122.991528000.6405
0.71873.044928500.6410
0.66763.098329000.6383
0.65133.151729500.6359
0.58213.205130000.6345
0.66423.258530500.6338
0.64753.312031000.6311
0.69993.365431500.6281
0.6963.418832000.6329
0.61293.472232500.6263
0.56013.525633000.6257
0.66583.579133500.6209
0.72123.632534000.6217
0.63923.685934500.6178
0.68773.739335000.6202
0.6323.792735500.6171
0.67623.846236000.6175
0.67763.899636500.6122
0.69643.953037000.6120
0.64484.006437500.6134
0.61834.059838000.6125
0.65444.113238500.6105
0.65834.166739000.6102
0.66874.220139500.6083
0.64084.273540000.6078
0.60774.326940500.6051
0.57814.380341000.6078
0.6454.433841500.6033
0.6564.487242000.6044
0.5624.540642500.6001
0.65684.594043000.6018
0.65724.647443500.6012
0.57494.700944000.6004
0.58114.754344500.5974
0.57654.807745000.6010
0.61774.861145500.5941
0.53714.914546000.5949
0.58634.967946500.5930
0.62745.021447000.5996
0.5925.074847500.5972
0.62865.128248000.5936
0.60565.181648500.5954
0.56975.235049000.5943
0.55625.288549500.5923
0.51735.341950000.5944
0.60375.395350500.5943
0.61255.448751000.5921
0.57295.502151500.5920
0.5615.555652000.5908
0.62445.609052500.5877
0.53385.662453000.5876
0.575.715853500.5912
0.5555.769254000.5863
0.59145.822654500.5855
0.57855.876155000.5826
0.60145.929555500.5830
0.61925.982956000.5834
0.60256.036356500.5854
0.53276.089757000.5861
0.59586.143257500.5856
0.53196.196658000.5852
0.57226.2558500.5848
0.5856.303459000.5854
0.55496.356859500.5829
0.55596.410360000.5816
0.55396.463760500.5820
0.58476.517161000.5797
0.59646.570561500.5803
0.56136.623962000.5808
0.51946.677462500.5816
0.56646.730863000.5808
0.59716.784263500.5783
0.49376.837664000.5773
0.5776.891064500.5779
0.54226.944465000.5799
0.48816.997965500.5789
0.58067.051366000.5802
0.50297.104766500.5798
0.54367.158167000.5782
0.52987.211567500.5783
0.59477.265068000.5777
0.49757.318468500.5762
0.56527.371869000.5761
0.52147.425269500.5793
0.5357.478670000.5791
0.50357.532170500.5774
0.50047.585571000.5769
0.61877.638971500.5778
0.50427.692372000.5770
0.51167.745772500.5760
0.51787.799173000.5744
0.51547.852673500.5748
0.56417.906074000.5764
0.5367.959474500.5762
0.54238.012875000.5743
0.54028.066275500.5755
0.55598.119776000.5755
0.49988.173176500.5769
0.58958.226577000.5777
0.52528.279977500.5786
0.48028.333378000.5774
0.52858.386878500.5742
0.49318.440279000.5740
0.51268.493679500.5761
0.53768.547080000.5754
0.48258.600480500.5761
0.5098.653881000.5762
0.48238.707381500.5747
0.53798.760782000.5733
0.52838.814182500.5740
0.46628.867583000.5743
0.53258.920983500.5727
0.56288.974484000.5727
0.48859.027884500.5731
0.51879.081285000.5761
0.52869.134685500.5752
0.47389.188086000.5744
0.49319.241586500.5733
0.54039.294987000.5751
0.49279.348387500.5755
0.54159.401788000.5743
0.56279.455188500.5746
0.4689.508589000.5743
0.50029.562089500.5751
0.48919.615490000.5740
0.54569.668890500.5747
0.50549.722291000.5747
0.54539.775691500.5741
0.4779.829192000.5745
0.47989.882592500.5747
0.50039.935993000.5748
0.47859.989393500.5748

Framework versions

  • —Transformers 4.40.1
  • —Pytorch 2.2.1+cu121
  • —Datasets 2.19.0
  • —Tokenizers 0.19.1