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IrwinD
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log_sage_reward_model

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

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  1. README.md +26 -26
  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: 0.3796
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  - Accuracy: 1.0
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  ## Model description
@@ -68,31 +68,31 @@ 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 | 0.6922 | 1.0 |
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- | No log | 2.0 | 3 | 0.6862 | 1.0 |
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- | No log | 3.0 | 5 | 0.6811 | 1.0 |
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- | No log | 4.0 | 6 | 0.6785 | 1.0 |
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- | No log | 5.0 | 8 | 0.6729 | 1.0 |
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- | 0.2836 | 6.0 | 10 | 0.6659 | 1.0 |
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- | 0.2836 | 7.0 | 11 | 0.6611 | 1.0 |
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- | 0.2836 | 8.0 | 13 | 0.6493 | 1.0 |
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- | 0.2836 | 9.0 | 15 | 0.6328 | 1.0 |
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- | 0.2836 | 10.0 | 16 | 0.6230 | 1.0 |
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- | 0.2836 | 11.0 | 18 | 0.6019 | 1.0 |
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- | 0.2714 | 12.0 | 20 | 0.5754 | 1.0 |
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- | 0.2714 | 13.0 | 21 | 0.5604 | 1.0 |
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- | 0.2714 | 14.0 | 23 | 0.5312 | 1.0 |
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- | 0.2714 | 15.0 | 25 | 0.5008 | 1.0 |
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- | 0.2714 | 16.0 | 26 | 0.4862 | 1.0 |
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- | 0.2714 | 17.0 | 27 | 0.4728 | 1.0 |
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- | 0.2714 | 18.0 | 29 | 0.4492 | 1.0 |
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- | 0.2576 | 19.0 | 31 | 0.4277 | 1.0 |
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- | 0.2576 | 20.0 | 32 | 0.4183 | 1.0 |
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- | 0.2576 | 21.0 | 34 | 0.4027 | 1.0 |
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- | 0.2576 | 22.0 | 36 | 0.3909 | 1.0 |
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- | 0.2576 | 23.0 | 37 | 0.3864 | 1.0 |
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- | 0.2576 | 24.0 | 39 | 0.3808 | 1.0 |
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- | 0.1866 | 24.73 | 40 | 0.3796 | 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: 0.4696
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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 | 0.6955 | 0.4 |
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+ | No log | 2.0 | 3 | 0.6932 | 0.8 |
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+ | No log | 3.0 | 5 | 0.6909 | 0.8 |
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+ | No log | 4.0 | 6 | 0.6899 | 0.8 |
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+ | No log | 5.0 | 8 | 0.6880 | 0.8 |
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+ | 0.2853 | 6.0 | 10 | 0.6853 | 0.8 |
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+ | 0.2853 | 7.0 | 11 | 0.6831 | 0.8 |
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+ | 0.2853 | 8.0 | 13 | 0.6762 | 0.8 |
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+ | 0.2853 | 9.0 | 15 | 0.6632 | 1.0 |
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+ | 0.2853 | 10.0 | 16 | 0.6531 | 1.0 |
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+ | 0.2853 | 11.0 | 18 | 0.6293 | 1.0 |
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+ | 0.2623 | 12.0 | 20 | 0.5976 | 1.0 |
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+ | 0.2623 | 13.0 | 21 | 0.5817 | 1.0 |
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+ | 0.2623 | 14.0 | 23 | 0.5552 | 1.0 |
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+ | 0.2623 | 15.0 | 25 | 0.5370 | 1.0 |
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+ | 0.2623 | 16.0 | 26 | 0.5283 | 1.0 |
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+ | 0.2623 | 17.0 | 27 | 0.5198 | 1.0 |
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+ | 0.2623 | 18.0 | 29 | 0.5038 | 1.0 |
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+ | 0.233 | 19.0 | 31 | 0.4913 | 1.0 |
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+ | 0.233 | 20.0 | 32 | 0.4860 | 1.0 |
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+ | 0.233 | 21.0 | 34 | 0.4773 | 1.0 |
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+ | 0.233 | 22.0 | 36 | 0.4719 | 1.0 |
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+ | 0.233 | 23.0 | 37 | 0.4708 | 1.0 |
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+ | 0.233 | 24.0 | 39 | 0.4696 | 1.0 |
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+ | 0.1635 | 24.73 | 40 | 0.4696 | 1.0 |
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  ### Framework versions
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