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--- |
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license: apache-2.0 |
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base_model: distilbert/distilbert-base-uncased |
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tags: |
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- trl |
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- reward-trainer |
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- generated_from_trainer |
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datasets: |
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- hdfs_rlhf_log_summary_dataset |
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metrics: |
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- accuracy |
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model-index: |
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- name: log_sage_reward_model |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: hdfs_rlhf_log_summary_dataset |
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type: hdfs_rlhf_log_summary_dataset |
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config: default |
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split: None |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# log_sage_reward_model |
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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: 0.5838 |
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- Accuracy: 0.9 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1.41e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 40 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 1 | 0.6933 | 0.6 | |
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| No log | 2.0 | 3 | 0.6923 | 0.9 | |
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| No log | 3.0 | 4 | 0.6920 | 0.9 | |
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| No log | 4.0 | 6 | 0.6912 | 0.9 | |
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| No log | 5.0 | 8 | 0.6902 | 0.9 | |
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| No log | 6.0 | 9 | 0.6898 | 0.9 | |
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| 0.2745 | 7.0 | 11 | 0.6885 | 0.9 | |
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| 0.2745 | 8.0 | 12 | 0.6876 | 0.9 | |
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| 0.2745 | 9.0 | 13 | 0.6862 | 0.9 | |
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| 0.2745 | 10.0 | 15 | 0.6830 | 0.9 | |
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| 0.2745 | 11.0 | 16 | 0.6813 | 0.9 | |
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| 0.2745 | 12.0 | 18 | 0.6768 | 0.8 | |
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| 0.2705 | 13.0 | 20 | 0.6707 | 0.8 | |
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| 0.2705 | 14.0 | 21 | 0.6665 | 0.8 | |
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| 0.2705 | 15.0 | 23 | 0.6576 | 0.8 | |
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| 0.2705 | 16.0 | 24 | 0.6521 | 0.8 | |
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| 0.2705 | 17.0 | 25 | 0.6457 | 0.8 | |
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| 0.2705 | 18.0 | 27 | 0.6334 | 0.9 | |
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| 0.2705 | 19.0 | 28 | 0.6273 | 0.9 | |
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| 0.2555 | 20.0 | 30 | 0.6165 | 0.9 | |
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| 0.2555 | 21.0 | 32 | 0.6063 | 0.9 | |
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| 0.2555 | 22.0 | 33 | 0.6017 | 0.9 | |
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| 0.2555 | 23.0 | 35 | 0.5942 | 0.9 | |
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| 0.2555 | 24.0 | 36 | 0.5911 | 0.9 | |
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| 0.2555 | 25.0 | 37 | 0.5882 | 0.9 | |
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| 0.2555 | 26.0 | 39 | 0.5846 | 0.9 | |
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| 0.2245 | 27.0 | 40 | 0.5838 | 0.9 | |
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### Framework versions |
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- Transformers 4.39.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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