update model card README.md
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README.md
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## Model description
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state_mean = np.array([ 6.525973284621532, 3.9928073981048064, 12.498801233017467, 16.836990550577212, 16.837287388159297, 16.83684213167729, 16.837161803003287, 73.00388172165772, 73.00331088023746, 73.00445256307798, 73.00331088023746, 208.30597100125584, 208.30597100125584, 208.20287704075807, 208.30597100125584, 201.25448110514898, 201.25448110514898, 201.16189062678387, 201.25448110514898, 0.15652765849893777, 1.0663012570140091, 0.6994348432433195, 0.5023924181838172, 0.49339119658209996, 0.2731373418679261, 0.2731373418679261, 0.2731373418679261, 0.2731373418679261])
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state_std = np.array([ 3.448045414453991, 2.0032677368929734, 6.921673394725967, 3.564552828057008, 3.5647828974724476, 3.5643565817901974, 3.564711987899257, 16.480221141108398, 16.480030755727572, 16.480238315742053, 16.480030755727565, 292.79094956097464, 292.79094956097464, 292.70528837855596, 292.79094956097543, 296.18549714910006, 296.18549714910023, 296.1216266457902, 296.18549714910006, 0.035369600587780235, 0.8889958578862672, 1.0171468928300462, 0.40202104980478576, 2.6674362928093682, 0.11780233435944305, 0.11780233435944333, 0.11780233435944351, 0.11780233435944402])
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## Intended uses & limitations
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs:
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### Training results
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## Model description
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More information needed
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## Intended uses & limitations
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 120
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### Training results
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