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Th3BossC
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contradictions_model

Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
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  1. README.md +28 -28
  2. model.safetensors +1 -1
README.md CHANGED
@@ -17,7 +17,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0972
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  - Accuracy: 0.3490
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  ## Model description
@@ -50,33 +50,33 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 1.0973 | 0.07 | 100 | 1.0970 | 0.3490 |
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- | 1.0944 | 0.15 | 200 | 1.1089 | 0.3490 |
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- | 1.1024 | 0.22 | 300 | 1.0973 | 0.3490 |
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- | 1.0979 | 0.29 | 400 | 1.1086 | 0.3490 |
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- | 1.0933 | 0.37 | 500 | 1.0976 | 0.3490 |
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- | 1.0971 | 0.44 | 600 | 1.0973 | 0.3490 |
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- | 1.0908 | 0.51 | 700 | 1.0974 | 0.3490 |
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- | 1.0932 | 0.59 | 800 | 1.1078 | 0.3020 |
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- | 1.1203 | 0.66 | 900 | 1.0982 | 0.3490 |
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- | 1.0912 | 0.73 | 1000 | 1.1030 | 0.3490 |
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- | 1.0954 | 0.81 | 1100 | 1.1185 | 0.3020 |
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- | 1.1193 | 0.88 | 1200 | 1.0988 | 0.3490 |
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- | 1.097 | 0.95 | 1300 | 1.0965 | 0.3490 |
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- | 1.1098 | 1.03 | 1400 | 1.1006 | 0.3490 |
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- | 1.0967 | 1.1 | 1500 | 1.0981 | 0.3490 |
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- | 1.1007 | 1.17 | 1600 | 1.0971 | 0.3490 |
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- | 1.0983 | 1.25 | 1700 | 1.0985 | 0.3490 |
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- | 1.1033 | 1.32 | 1800 | 1.0996 | 0.3490 |
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- | 1.1333 | 1.39 | 1900 | 1.0964 | 0.3490 |
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- | 1.0982 | 1.47 | 2000 | 1.1024 | 0.3020 |
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- | 1.1039 | 1.54 | 2100 | 1.0985 | 0.3490 |
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- | 1.1038 | 1.61 | 2200 | 1.1003 | 0.3020 |
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- | 1.0974 | 1.69 | 2300 | 1.0988 | 0.3490 |
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- | 1.0914 | 1.76 | 2400 | 1.0979 | 0.3490 |
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- | 1.0933 | 1.83 | 2500 | 1.0976 | 0.3490 |
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- | 1.0988 | 1.91 | 2600 | 1.0971 | 0.3490 |
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- | 1.09 | 1.98 | 2700 | 1.0972 | 0.3490 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.0973
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  - Accuracy: 0.3490
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  ## Model description
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.1191 | 0.07 | 100 | 1.1001 | 0.3177 |
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+ | 1.1041 | 0.15 | 200 | 1.0959 | 0.3490 |
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+ | 1.1081 | 0.22 | 300 | 1.0927 | 0.3993 |
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+ | 1.1031 | 0.29 | 400 | 1.1143 | 0.3350 |
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+ | 1.0855 | 0.37 | 500 | 1.0973 | 0.3490 |
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+ | 1.0788 | 0.44 | 600 | 1.1068 | 0.3490 |
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+ | 1.1029 | 0.51 | 700 | 1.0978 | 0.3490 |
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+ | 1.1018 | 0.59 | 800 | 1.1049 | 0.3020 |
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+ | 1.0983 | 0.66 | 900 | 1.1168 | 0.3267 |
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+ | 1.1094 | 0.73 | 1000 | 1.1011 | 0.3020 |
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+ | 1.0866 | 0.81 | 1100 | 1.1168 | 0.3020 |
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+ | 1.1286 | 0.88 | 1200 | 1.1051 | 0.3020 |
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+ | 1.1128 | 0.95 | 1300 | 1.1016 | 0.3490 |
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+ | 1.1194 | 1.03 | 1400 | 1.0978 | 0.3490 |
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+ | 1.0899 | 1.1 | 1500 | 1.1028 | 0.3490 |
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+ | 1.0948 | 1.17 | 1600 | 1.0976 | 0.3490 |
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+ | 1.1061 | 1.25 | 1700 | 1.0975 | 0.3490 |
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+ | 1.0964 | 1.32 | 1800 | 1.1016 | 0.3020 |
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+ | 1.1117 | 1.39 | 1900 | 1.0989 | 0.3490 |
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+ | 1.1053 | 1.47 | 2000 | 1.1013 | 0.3020 |
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+ | 1.0966 | 1.54 | 2100 | 1.0979 | 0.3490 |
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+ | 1.1037 | 1.61 | 2200 | 1.1007 | 0.3490 |
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+ | 1.1102 | 1.69 | 2300 | 1.0984 | 0.3490 |
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+ | 1.1029 | 1.76 | 2400 | 1.0979 | 0.3490 |
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+ | 1.095 | 1.83 | 2500 | 1.0975 | 0.3490 |
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+ | 1.0942 | 1.91 | 2600 | 1.0973 | 0.3490 |
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+ | 1.0962 | 1.98 | 2700 | 1.0973 | 0.3490 |
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  ### Framework versions
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