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msa_prot_t5

Transformers
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
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End of training

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  1. README.md +103 -103
  2. model-00001-of-00003.safetensors +1 -1
  3. model-00002-of-00003.safetensors +1 -1
  4. model-00003-of-00003.safetensors +1 -1
README.md CHANGED
@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [Rostlab/prot_t5_xl_uniref50](https://huggingface.co/Rostlab/prot_t5_xl_uniref50) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.8955
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  ## Model description
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@@ -44,108 +44,108 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss |
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- |:-------------:|:-----:|:-----:|:---------------:|
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- | 2.9094 | 1.0 | 356 | 2.9027 |
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- | 2.8947 | 2.0 | 712 | 2.8965 |
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- | 2.901 | 3.0 | 1068 | 2.8935 |
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- | 2.9007 | 4.0 | 1424 | 2.8933 |
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- | 2.9028 | 5.0 | 1780 | 2.9093 |
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- | 2.8926 | 6.0 | 2136 | 2.9062 |
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- | 2.9033 | 7.0 | 2492 | 2.8993 |
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- | 2.9039 | 8.0 | 2848 | 2.9036 |
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- | 2.8954 | 9.0 | 3204 | 2.8962 |
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- | 2.9046 | 10.0 | 3560 | 2.9003 |
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- | 2.9031 | 11.0 | 3916 | 2.9030 |
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- | 2.9066 | 12.0 | 4272 | 2.9033 |
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- | 2.893 | 13.0 | 4628 | 2.9013 |
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- | 2.8927 | 14.0 | 4984 | 2.8975 |
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- | 2.9021 | 15.0 | 5340 | 2.8938 |
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- | 2.898 | 16.0 | 5696 | 2.8850 |
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- | 2.9036 | 17.0 | 6052 | 2.8970 |
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- | 2.9078 | 18.0 | 6408 | 2.8901 |
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- | 2.9101 | 19.0 | 6764 | 2.8927 |
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- | 2.892 | 20.0 | 7120 | 2.8840 |
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- | 2.9044 | 21.0 | 7476 | 2.8920 |
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- | 2.8989 | 22.0 | 7832 | 2.8835 |
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- | 2.8949 | 23.0 | 8188 | 2.8939 |
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- | 2.8987 | 24.0 | 8544 | 2.8961 |
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- | 2.9021 | 25.0 | 8900 | 2.8932 |
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- | 2.8935 | 26.0 | 9256 | 2.8856 |
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- | 2.8949 | 27.0 | 9612 | 2.8968 |
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- | 2.8974 | 28.0 | 9968 | 2.8888 |
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- | 2.8922 | 29.0 | 10324 | 2.9046 |
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- | 2.9028 | 30.0 | 10680 | 2.8931 |
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- | 2.8978 | 31.0 | 11036 | 2.8963 |
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- | 2.8977 | 32.0 | 11392 | 2.8920 |
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- | 2.9044 | 33.0 | 11748 | 2.9045 |
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- | 2.8966 | 34.0 | 12104 | 2.8918 |
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- | 2.898 | 35.0 | 12460 | 2.8981 |
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- | 2.9051 | 36.0 | 12816 | 2.8973 |
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- | 2.9046 | 37.0 | 13172 | 2.8868 |
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- | 2.9011 | 38.0 | 13528 | 2.9006 |
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- | 2.8903 | 39.0 | 13884 | 2.8997 |
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- | 2.907 | 40.0 | 14240 | 2.8961 |
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- | 2.9088 | 41.0 | 14596 | 2.8939 |
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- | 2.8976 | 42.0 | 14952 | 2.8994 |
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- | 2.9023 | 43.0 | 15308 | 2.8867 |
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- | 2.8879 | 44.0 | 15664 | 2.8917 |
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- | 2.89 | 45.0 | 16020 | 2.8878 |
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- | 2.889 | 46.0 | 16376 | 2.8945 |
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- | 2.8947 | 47.0 | 16732 | 2.8930 |
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- | 2.8911 | 48.0 | 17088 | 2.9011 |
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- | 2.8939 | 49.0 | 17444 | 2.8833 |
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- | 2.897 | 50.0 | 17800 | 2.8949 |
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- | 2.8925 | 51.0 | 18156 | 2.8942 |
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- | 2.8928 | 52.0 | 18512 | 2.8832 |
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- | 2.9042 | 53.0 | 18868 | 2.8860 |
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- | 2.8944 | 54.0 | 19224 | 2.8948 |
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- | 2.9032 | 55.0 | 19580 | 2.8948 |
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- | 2.9003 | 56.0 | 19936 | 2.8877 |
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- | 2.9017 | 57.0 | 20292 | 2.9022 |
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- | 2.8963 | 58.0 | 20648 | 2.8900 |
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- | 2.9042 | 59.0 | 21004 | 2.9029 |
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- | 2.8911 | 60.0 | 21360 | 2.8832 |
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- | 2.9007 | 61.0 | 21716 | 2.8943 |
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- | 2.8952 | 62.0 | 22072 | 2.8984 |
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- | 2.9003 | 63.0 | 22428 | 2.8929 |
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- | 2.8932 | 64.0 | 22784 | 2.8967 |
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- | 2.9033 | 65.0 | 23140 | 2.9023 |
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- | 2.8993 | 66.0 | 23496 | 2.8929 |
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- | 2.8999 | 67.0 | 23852 | 2.8924 |
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- | 2.8857 | 68.0 | 24208 | 2.8979 |
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- | 2.8924 | 69.0 | 24564 | 2.8934 |
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- | 2.9083 | 70.0 | 24920 | 2.8890 |
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- | 2.901 | 71.0 | 25276 | 2.9047 |
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- | 2.9026 | 72.0 | 25632 | 2.8877 |
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- | 2.8991 | 73.0 | 25988 | 2.8871 |
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- | 2.8983 | 74.0 | 26344 | 2.8865 |
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- | 2.8895 | 75.0 | 26700 | 2.9036 |
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- | 2.8957 | 76.0 | 27056 | 2.8920 |
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- | 2.8963 | 77.0 | 27412 | 2.8911 |
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- | 2.9062 | 78.0 | 27768 | 2.9045 |
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- | 2.8931 | 79.0 | 28124 | 2.8963 |
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- | 2.9065 | 80.0 | 28480 | 2.8876 |
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- | 2.892 | 81.0 | 28836 | 2.8762 |
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- | 2.8985 | 82.0 | 29192 | 2.8965 |
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- | 2.8969 | 83.0 | 29548 | 2.8994 |
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- | 2.8885 | 84.0 | 29904 | 2.9030 |
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- | 2.9047 | 85.0 | 30260 | 2.8905 |
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- | 2.8987 | 86.0 | 30616 | 2.8993 |
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- | 2.9029 | 87.0 | 30972 | 2.8878 |
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- | 2.8994 | 88.0 | 31328 | 2.8911 |
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- | 2.8884 | 89.0 | 31684 | 2.8911 |
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- | 2.8954 | 90.0 | 32040 | 2.8959 |
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- | 2.8952 | 91.0 | 32396 | 2.8860 |
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- | 2.896 | 92.0 | 32752 | 2.8820 |
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- | 2.9001 | 93.0 | 33108 | 2.8878 |
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- | 2.902 | 94.0 | 33464 | 2.8862 |
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- | 2.8946 | 95.0 | 33820 | 2.8943 |
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- | 2.892 | 96.0 | 34176 | 2.8932 |
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- | 2.8918 | 97.0 | 34532 | 2.8967 |
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- | 2.9073 | 98.0 | 34888 | 2.8972 |
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- | 2.903 | 99.0 | 35244 | 2.8981 |
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- | 2.8984 | 100.0 | 35600 | 2.8955 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [Rostlab/prot_t5_xl_uniref50](https://huggingface.co/Rostlab/prot_t5_xl_uniref50) on an unknown dataset.
17
  It achieves the following results on the evaluation set:
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+ - Loss: 2.9147
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:----:|:---------------:|
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+ | No log | 1.0 | 1 | 2.9866 |
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+ | No log | 2.0 | 2 | 3.1698 |
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+ | No log | 3.0 | 3 | 3.1083 |
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+ | No log | 4.0 | 4 | 2.9634 |
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+ | No log | 5.0 | 5 | 2.9300 |
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+ | No log | 6.0 | 6 | 3.2760 |
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+ | No log | 7.0 | 7 | 2.8957 |
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+ | No log | 8.0 | 8 | 2.9622 |
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+ | No log | 9.0 | 9 | 2.9933 |
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+ | No log | 10.0 | 10 | 2.8233 |
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+ | No log | 11.0 | 11 | 2.7758 |
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+ | No log | 12.0 | 12 | 2.9012 |
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+ | No log | 13.0 | 13 | 2.9936 |
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+ | No log | 14.0 | 14 | 2.9671 |
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+ | No log | 15.0 | 15 | 3.0426 |
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+ | No log | 16.0 | 16 | 2.9478 |
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+ | No log | 17.0 | 17 | 2.9906 |
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+ | No log | 18.0 | 18 | 2.9911 |
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+ | No log | 19.0 | 19 | 2.8639 |
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+ | No log | 20.0 | 20 | 2.8557 |
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+ | No log | 21.0 | 21 | 2.9053 |
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+ | No log | 22.0 | 22 | 2.8347 |
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+ | No log | 23.0 | 23 | 2.9482 |
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+ | No log | 24.0 | 24 | 2.9520 |
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+ | No log | 25.0 | 25 | 3.0104 |
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+ | No log | 26.0 | 26 | 2.8693 |
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+ | No log | 27.0 | 27 | 2.8381 |
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+ | No log | 28.0 | 28 | 2.8333 |
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+ | No log | 29.0 | 29 | 2.8644 |
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+ | No log | 30.0 | 30 | 3.0112 |
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+ | No log | 31.0 | 31 | 3.1106 |
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+ | No log | 32.0 | 32 | 3.0417 |
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+ | No log | 33.0 | 33 | 2.7188 |
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+ | No log | 34.0 | 34 | 2.9129 |
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+ | No log | 35.0 | 35 | 2.7620 |
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+ | No log | 36.0 | 36 | 2.7964 |
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+ | No log | 37.0 | 37 | 2.8155 |
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+ | No log | 38.0 | 38 | 2.9062 |
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+ | No log | 39.0 | 39 | 2.8137 |
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+ | No log | 40.0 | 40 | 2.9159 |
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+ | No log | 41.0 | 41 | 2.8783 |
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+ | No log | 42.0 | 42 | 3.1200 |
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+ | No log | 43.0 | 43 | 2.9103 |
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+ | No log | 44.0 | 44 | 2.9167 |
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+ | No log | 45.0 | 45 | 2.8640 |
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+ | No log | 46.0 | 46 | 2.7939 |
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+ | No log | 47.0 | 47 | 3.0191 |
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+ | No log | 48.0 | 48 | 2.8166 |
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+ | No log | 49.0 | 49 | 3.1344 |
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+ | 2.9463 | 50.0 | 50 | 3.0017 |
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+ | 2.9463 | 51.0 | 51 | 3.0631 |
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+ | 2.9463 | 52.0 | 52 | 2.6599 |
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+ | 2.9463 | 53.0 | 53 | 2.9787 |
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+ | 2.9463 | 54.0 | 54 | 2.7147 |
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+ | 2.9463 | 55.0 | 55 | 2.9215 |
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+ | 2.9463 | 56.0 | 56 | 2.8183 |
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+ | 2.9463 | 57.0 | 57 | 2.9195 |
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+ | 2.9463 | 58.0 | 58 | 2.9742 |
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+ | 2.9463 | 59.0 | 59 | 2.9367 |
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+ | 2.9463 | 60.0 | 60 | 2.8563 |
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+ | 2.9463 | 61.0 | 61 | 3.2135 |
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+ | 2.9463 | 62.0 | 62 | 2.9945 |
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+ | 2.9463 | 63.0 | 63 | 2.9708 |
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+ | 2.9463 | 64.0 | 64 | 2.8022 |
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+ | 2.9463 | 65.0 | 65 | 2.9473 |
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+ | 2.9463 | 66.0 | 66 | 2.9607 |
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+ | 2.9463 | 67.0 | 67 | 2.8410 |
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+ | 2.9463 | 68.0 | 68 | 2.8940 |
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+ | 2.9463 | 69.0 | 69 | 2.9710 |
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+ | 2.9463 | 70.0 | 70 | 3.0025 |
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+ | 2.9463 | 71.0 | 71 | 2.8677 |
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+ | 2.9463 | 72.0 | 72 | 2.8281 |
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+ | 2.9463 | 73.0 | 73 | 2.9339 |
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+ | 2.9463 | 74.0 | 74 | 2.9076 |
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+ | 2.9463 | 75.0 | 75 | 2.8363 |
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+ | 2.9463 | 76.0 | 76 | 2.9525 |
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+ | 2.9463 | 77.0 | 77 | 2.8536 |
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+ | 2.9463 | 78.0 | 78 | 2.8605 |
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+ | 2.9463 | 79.0 | 79 | 2.9587 |
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+ | 2.9463 | 80.0 | 80 | 2.9319 |
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+ | 2.9463 | 81.0 | 81 | 2.9245 |
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+ | 2.9463 | 82.0 | 82 | 2.8225 |
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+ | 2.9463 | 83.0 | 83 | 2.8640 |
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+ | 2.9463 | 84.0 | 84 | 2.8604 |
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+ | 2.9463 | 85.0 | 85 | 2.7640 |
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+ | 2.9463 | 86.0 | 86 | 2.9671 |
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+ | 2.9463 | 87.0 | 87 | 2.9539 |
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+ | 2.9463 | 88.0 | 88 | 2.9196 |
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+ | 2.9463 | 89.0 | 89 | 2.7831 |
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+ | 2.9463 | 90.0 | 90 | 2.8095 |
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+ | 2.9463 | 91.0 | 91 | 2.9585 |
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+ | 2.9463 | 92.0 | 92 | 2.8277 |
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+ | 2.9463 | 93.0 | 93 | 2.8445 |
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+ | 2.9463 | 94.0 | 94 | 2.9094 |
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+ | 2.9463 | 95.0 | 95 | 2.9313 |
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+ | 2.9463 | 96.0 | 96 | 2.8166 |
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+ | 2.9463 | 97.0 | 97 | 2.9152 |
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+ | 2.9463 | 98.0 | 98 | 2.8646 |
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+ | 2.9463 | 99.0 | 99 | 2.9297 |
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+ | 2.8987 | 100.0 | 100 | 2.9147 |
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  ### Framework versions
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