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README.md ADDED
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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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+
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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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+
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+ # log_sage_reward_model
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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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+ "torch_dtype": "float32",
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+ "transformers_version": "4.39.0",
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+ "vocab_size": 30522
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+ }
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