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IrwinD
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log_sage_reward_model

Text Classification
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Safetensors
distilbert
trl
reward-trainer
Generated from Trainer
Eval Results
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End of training

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  1. README.md +63 -63
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -34,7 +34,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the hdfs_rlhf_log_summary_dataset dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4300
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  - Accuracy: 1.0
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  ## Model description
@@ -68,68 +68,68 @@ 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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- | No log | 1.0 | 1 | 0.6905 | 1.0 |
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- | No log | 2.0 | 3 | 0.6854 | 1.0 |
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- | No log | 3.0 | 5 | 0.6802 | 1.0 |
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- | No log | 4.0 | 6 | 0.6772 | 1.0 |
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- | No log | 5.0 | 8 | 0.6692 | 1.0 |
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- | 0.2821 | 6.0 | 10 | 0.6568 | 1.0 |
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- | 0.2821 | 7.0 | 11 | 0.6470 | 1.0 |
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- | 0.2821 | 8.0 | 13 | 0.6235 | 1.0 |
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- | 0.2821 | 9.0 | 15 | 0.5924 | 1.0 |
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- | 0.2821 | 10.0 | 16 | 0.5745 | 1.0 |
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- | 0.2821 | 11.0 | 18 | 0.5410 | 1.0 |
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- | 0.2394 | 12.0 | 20 | 0.5131 | 1.0 |
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- | 0.2394 | 13.0 | 21 | 0.5048 | 1.0 |
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- | 0.2394 | 14.0 | 23 | 0.4948 | 1.0 |
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- | 0.2394 | 15.0 | 25 | 0.4873 | 1.0 |
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- | 0.2394 | 16.0 | 26 | 0.4790 | 1.0 |
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- | 0.2394 | 17.0 | 27 | 0.4710 | 1.0 |
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- | 0.2394 | 18.0 | 29 | 0.4541 | 1.0 |
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- | 0.17 | 19.0 | 31 | 0.4446 | 1.0 |
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- | 0.17 | 20.0 | 32 | 0.4420 | 1.0 |
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- | 0.17 | 21.0 | 34 | 0.4379 | 1.0 |
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- | 0.17 | 22.0 | 36 | 0.4341 | 1.0 |
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- | 0.17 | 23.0 | 37 | 0.4326 | 1.0 |
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- | 0.17 | 24.0 | 39 | 0.4303 | 1.0 |
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- | 0.0955 | 25.0 | 41 | 0.4285 | 1.0 |
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- | 0.0955 | 26.0 | 42 | 0.4278 | 1.0 |
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- | 0.0955 | 27.0 | 44 | 0.4267 | 1.0 |
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- | 0.0955 | 28.0 | 46 | 0.4260 | 1.0 |
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- | 0.0955 | 29.0 | 47 | 0.4260 | 1.0 |
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- | 0.0955 | 30.0 | 49 | 0.4269 | 1.0 |
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- | 0.089 | 31.0 | 51 | 0.4264 | 1.0 |
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- | 0.089 | 32.0 | 52 | 0.4260 | 1.0 |
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- | 0.089 | 33.0 | 53 | 0.4263 | 1.0 |
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- | 0.089 | 34.0 | 55 | 0.4248 | 1.0 |
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- | 0.089 | 35.0 | 57 | 0.4242 | 1.0 |
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- | 0.089 | 36.0 | 58 | 0.4239 | 1.0 |
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- | 0.0816 | 37.0 | 60 | 0.4238 | 1.0 |
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- | 0.0816 | 38.0 | 62 | 0.4234 | 1.0 |
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- | 0.0816 | 39.0 | 63 | 0.4234 | 1.0 |
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- | 0.0816 | 40.0 | 65 | 0.4245 | 1.0 |
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- | 0.0816 | 41.0 | 67 | 0.4274 | 1.0 |
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- | 0.0816 | 42.0 | 68 | 0.4276 | 1.0 |
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- | 0.0707 | 43.0 | 70 | 0.4265 | 1.0 |
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- | 0.0707 | 44.0 | 72 | 0.4238 | 1.0 |
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- | 0.0707 | 45.0 | 73 | 0.4231 | 1.0 |
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- | 0.0707 | 46.0 | 75 | 0.4224 | 1.0 |
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- | 0.0707 | 47.0 | 77 | 0.4224 | 1.0 |
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- | 0.0707 | 48.0 | 78 | 0.4225 | 1.0 |
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- | 0.0707 | 49.0 | 79 | 0.4228 | 1.0 |
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- | 0.0737 | 50.0 | 81 | 0.4232 | 1.0 |
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- | 0.0737 | 51.0 | 83 | 0.4235 | 1.0 |
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- | 0.0737 | 52.0 | 84 | 0.4238 | 1.0 |
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- | 0.0737 | 53.0 | 86 | 0.4253 | 1.0 |
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- | 0.0737 | 54.0 | 88 | 0.4278 | 1.0 |
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- | 0.0737 | 55.0 | 89 | 0.4285 | 1.0 |
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- | 0.0675 | 56.0 | 91 | 0.4293 | 1.0 |
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- | 0.0675 | 57.0 | 93 | 0.4292 | 1.0 |
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- | 0.0675 | 58.0 | 94 | 0.4294 | 1.0 |
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- | 0.0675 | 59.0 | 96 | 0.4299 | 1.0 |
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- | 0.0675 | 60.0 | 98 | 0.4299 | 1.0 |
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- | 0.0675 | 61.0 | 99 | 0.4299 | 1.0 |
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- | 0.0632 | 61.09 | 100 | 0.4300 | 1.0 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the hdfs_rlhf_log_summary_dataset dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4242
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  - Accuracy: 1.0
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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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+ | No log | 1.0 | 1 | 0.6936 | 0.8 |
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+ | No log | 2.0 | 3 | 0.6931 | 0.8 |
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+ | No log | 3.0 | 5 | 0.6928 | 1.0 |
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+ | No log | 4.0 | 6 | 0.6927 | 1.0 |
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+ | No log | 5.0 | 8 | 0.6923 | 1.0 |
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+ | 0.2849 | 6.0 | 10 | 0.6915 | 1.0 |
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+ | 0.2849 | 7.0 | 11 | 0.6908 | 1.0 |
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+ | 0.2849 | 8.0 | 13 | 0.6889 | 1.0 |
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+ | 0.2849 | 9.0 | 15 | 0.6838 | 1.0 |
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+ | 0.2849 | 10.0 | 16 | 0.6788 | 1.0 |
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+ | 0.2849 | 11.0 | 18 | 0.6633 | 1.0 |
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+ | 0.2669 | 12.0 | 20 | 0.6464 | 1.0 |
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+ | 0.2669 | 13.0 | 21 | 0.6422 | 1.0 |
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+ | 0.2669 | 14.0 | 23 | 0.6312 | 1.0 |
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+ | 0.2669 | 15.0 | 25 | 0.5991 | 1.0 |
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+ | 0.2669 | 16.0 | 26 | 0.5796 | 1.0 |
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+ | 0.2669 | 17.0 | 27 | 0.5571 | 1.0 |
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+ | 0.2669 | 18.0 | 29 | 0.5255 | 1.0 |
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+ | 0.2252 | 19.0 | 31 | 0.5055 | 1.0 |
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+ | 0.2252 | 20.0 | 32 | 0.4967 | 1.0 |
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+ | 0.2252 | 21.0 | 34 | 0.4841 | 1.0 |
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+ | 0.2252 | 22.0 | 36 | 0.4742 | 1.0 |
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+ | 0.2252 | 23.0 | 37 | 0.4700 | 1.0 |
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+ | 0.2252 | 24.0 | 39 | 0.4633 | 1.0 |
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+ | 0.1245 | 25.0 | 41 | 0.4573 | 1.0 |
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+ | 0.1245 | 26.0 | 42 | 0.4547 | 1.0 |
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+ | 0.1245 | 27.0 | 44 | 0.4501 | 1.0 |
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+ | 0.1245 | 28.0 | 46 | 0.4462 | 1.0 |
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+ | 0.1245 | 29.0 | 47 | 0.4444 | 1.0 |
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+ | 0.1245 | 30.0 | 49 | 0.4415 | 1.0 |
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+ | 0.0996 | 31.0 | 51 | 0.4390 | 1.0 |
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+ | 0.0996 | 32.0 | 52 | 0.4378 | 1.0 |
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+ | 0.0996 | 33.0 | 53 | 0.4368 | 1.0 |
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+ | 0.0996 | 34.0 | 55 | 0.4349 | 1.0 |
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+ | 0.0996 | 35.0 | 57 | 0.4333 | 1.0 |
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+ | 0.0996 | 36.0 | 58 | 0.4326 | 1.0 |
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+ | 0.0862 | 37.0 | 60 | 0.4315 | 1.0 |
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+ | 0.0862 | 38.0 | 62 | 0.4306 | 1.0 |
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+ | 0.0862 | 39.0 | 63 | 0.4301 | 1.0 |
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+ | 0.0862 | 40.0 | 65 | 0.4294 | 1.0 |
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+ | 0.0862 | 41.0 | 67 | 0.4288 | 1.0 |
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+ | 0.0862 | 42.0 | 68 | 0.4285 | 1.0 |
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+ | 0.0765 | 43.0 | 70 | 0.4281 | 1.0 |
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+ | 0.0765 | 44.0 | 72 | 0.4276 | 1.0 |
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+ | 0.0765 | 45.0 | 73 | 0.4272 | 1.0 |
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+ | 0.0765 | 46.0 | 75 | 0.4265 | 1.0 |
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+ | 0.0765 | 47.0 | 77 | 0.4261 | 1.0 |
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+ | 0.0765 | 48.0 | 78 | 0.4259 | 1.0 |
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+ | 0.0765 | 49.0 | 79 | 0.4257 | 1.0 |
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+ | 0.0783 | 50.0 | 81 | 0.4253 | 1.0 |
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+ | 0.0783 | 51.0 | 83 | 0.4250 | 1.0 |
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+ | 0.0783 | 52.0 | 84 | 0.4249 | 1.0 |
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+ | 0.0783 | 53.0 | 86 | 0.4247 | 1.0 |
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+ | 0.0783 | 54.0 | 88 | 0.4246 | 1.0 |
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+ | 0.0783 | 55.0 | 89 | 0.4245 | 1.0 |
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+ | 0.0652 | 56.0 | 91 | 0.4244 | 1.0 |
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+ | 0.0652 | 57.0 | 93 | 0.4243 | 1.0 |
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+ | 0.0652 | 58.0 | 94 | 0.4243 | 1.0 |
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+ | 0.0652 | 59.0 | 96 | 0.4242 | 1.0 |
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+ | 0.0652 | 60.0 | 98 | 0.4242 | 1.0 |
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+ | 0.0652 | 61.0 | 99 | 0.4242 | 1.0 |
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+ | 0.0655 | 61.09 | 100 | 0.4242 | 1.0 |
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  ### Framework versions
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