metadata
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- trl
- reward-trainer
- generated_from_trainer
datasets:
- hdfs_rlhf_log_summary_dataset
metrics:
- accuracy
model-index:
- name: log_sage_reward_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: hdfs_rlhf_log_summary_dataset
type: hdfs_rlhf_log_summary_dataset
config: default
split: None
args: default
metrics:
- name: Accuracy
type: accuracy
value: 1
log_sage_reward_model
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the hdfs_rlhf_log_summary_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.1669
- Accuracy: 1.0
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.41e-05
- train_batch_size: 6
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 1 | 0.6950 | 0.5 |
| No log | 2.0 | 2 | 0.6896 | 1.0 |
| No log | 3.0 | 3 | 0.6843 | 1.0 |
| No log | 4.0 | 4 | 0.6789 | 1.0 |
| No log | 5.0 | 5 | 0.6735 | 1.0 |
| No log | 6.0 | 6 | 0.6671 | 1.0 |
| No log | 7.0 | 7 | 0.6597 | 1.0 |
| No log | 8.0 | 8 | 0.6510 | 1.0 |
| No log | 9.0 | 9 | 0.6403 | 1.0 |
| 0.0839 | 10.0 | 10 | 0.6275 | 1.0 |
| 0.0839 | 11.0 | 11 | 0.6130 | 1.0 |
| 0.0839 | 12.0 | 12 | 0.5955 | 1.0 |
| 0.0839 | 13.0 | 13 | 0.5747 | 1.0 |
| 0.0839 | 14.0 | 14 | 0.5508 | 1.0 |
| 0.0839 | 15.0 | 15 | 0.5250 | 1.0 |
| 0.0839 | 16.0 | 16 | 0.4984 | 1.0 |
| 0.0839 | 17.0 | 17 | 0.4698 | 1.0 |
| 0.0839 | 18.0 | 18 | 0.4413 | 1.0 |
| 0.0839 | 19.0 | 19 | 0.4121 | 1.0 |
| 0.0658 | 20.0 | 20 | 0.3850 | 1.0 |
| 0.0658 | 21.0 | 21 | 0.3604 | 1.0 |
| 0.0658 | 22.0 | 22 | 0.3384 | 1.0 |
| 0.0658 | 23.0 | 23 | 0.3186 | 1.0 |
| 0.0658 | 24.0 | 24 | 0.2995 | 1.0 |
| 0.0658 | 25.0 | 25 | 0.2823 | 1.0 |
| 0.0658 | 26.0 | 26 | 0.2664 | 1.0 |
| 0.0658 | 27.0 | 27 | 0.2516 | 1.0 |
| 0.0658 | 28.0 | 28 | 0.2384 | 1.0 |
| 0.0658 | 29.0 | 29 | 0.2260 | 1.0 |
| 0.0346 | 30.0 | 30 | 0.2149 | 1.0 |
| 0.0346 | 31.0 | 31 | 0.2054 | 1.0 |
| 0.0346 | 32.0 | 32 | 0.1971 | 1.0 |
| 0.0346 | 33.0 | 33 | 0.1898 | 1.0 |
| 0.0346 | 34.0 | 34 | 0.1838 | 1.0 |
| 0.0346 | 35.0 | 35 | 0.1787 | 1.0 |
| 0.0346 | 36.0 | 36 | 0.1746 | 1.0 |
| 0.0346 | 37.0 | 37 | 0.1714 | 1.0 |
| 0.0346 | 38.0 | 38 | 0.1691 | 1.0 |
| 0.0346 | 39.0 | 39 | 0.1676 | 1.0 |
| 0.021 | 40.0 | 40 | 0.1669 | 1.0 |
Framework versions
- Transformers 4.39.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2