Adaptive Chemical Embedding Model
AI & ML interests
We try to teach computers how to design materials that work in the real world
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Eval Cards
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Registry of eval-cards from different benchmark
models 54
jablonkagroup/ACEMol-light
0.2B β’ Updated
β’ 19
jablonkagroup/ACEMol
0.2B β’ Updated
β’ 438
jablonkagroup/CV_data_simulated
Updated
jablonkagroup/phone-numbers
Updated
jablonkagroup/chempile-llama-education_paper_lift_reasoning-100m-checkpoint-300
Updated
β’ 1
jablonkagroup/chempile-llama-education_paper_lift_reasoning-100m-checkpoint-200
Updated
β’ 1
jablonkagroup/chempile-llama-education_paper_lift_reasoning-100m-checkpoint-100
Updated
jablonkagroup/chempile-llama-lift-100m-checkpoint-450
Updated
jablonkagroup/chempile-llama-lift-100m-checkpoint-400
Updated
jablonkagroup/chempile-llama-lift-100m-checkpoint-300
Updated
β’ 1
datasets 301
jablonkagroup/corral_runs_reports
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β’ Updated
β’ 481 β’ 39
jablonkagroup/corral-traces
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β’ Updated
β’ 54.4k β’ 200
jablonkagroup/corral-reports
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β’ Updated
β’ 11.3k β’ 198
jablonkagroup/corral-oss-trace-logprobs
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β’ Updated
β’ 65.4k β’ 119
jablonkagroup/corral-result-v0
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β’ Updated
β’ 582 β’ 7
jablonkagroup/smiles
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β’ Updated
β’ 262M β’ 32
jablonkagroup/corral-oss-intervention-resistor
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β’ Updated
β’ 3.58k β’ 22
jablonkagroup/ACEMol-Toxic
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β’ Updated
β’ 4.2k β’ 614
jablonkagroup/ACEMol-Pretrain
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β’ Updated
β’ 184M β’ 647
jablonkagroup/MatText-hypo_pot
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β’ Updated
β’ 183k β’ 94