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: 0.4242
- 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: 4
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 1 | 0.6936 | 0.8 |
| No log | 2.0 | 3 | 0.6931 | 0.8 |
| No log | 3.0 | 5 | 0.6928 | 1.0 |
| No log | 4.0 | 6 | 0.6927 | 1.0 |
| No log | 5.0 | 8 | 0.6923 | 1.0 |
| 0.2849 | 6.0 | 10 | 0.6915 | 1.0 |
| 0.2849 | 7.0 | 11 | 0.6908 | 1.0 |
| 0.2849 | 8.0 | 13 | 0.6889 | 1.0 |
| 0.2849 | 9.0 | 15 | 0.6838 | 1.0 |
| 0.2849 | 10.0 | 16 | 0.6788 | 1.0 |
| 0.2849 | 11.0 | 18 | 0.6633 | 1.0 |
| 0.2669 | 12.0 | 20 | 0.6464 | 1.0 |
| 0.2669 | 13.0 | 21 | 0.6422 | 1.0 |
| 0.2669 | 14.0 | 23 | 0.6312 | 1.0 |
| 0.2669 | 15.0 | 25 | 0.5991 | 1.0 |
| 0.2669 | 16.0 | 26 | 0.5796 | 1.0 |
| 0.2669 | 17.0 | 27 | 0.5571 | 1.0 |
| 0.2669 | 18.0 | 29 | 0.5255 | 1.0 |
| 0.2252 | 19.0 | 31 | 0.5055 | 1.0 |
| 0.2252 | 20.0 | 32 | 0.4967 | 1.0 |
| 0.2252 | 21.0 | 34 | 0.4841 | 1.0 |
| 0.2252 | 22.0 | 36 | 0.4742 | 1.0 |
| 0.2252 | 23.0 | 37 | 0.4700 | 1.0 |
| 0.2252 | 24.0 | 39 | 0.4633 | 1.0 |
| 0.1245 | 25.0 | 41 | 0.4573 | 1.0 |
| 0.1245 | 26.0 | 42 | 0.4547 | 1.0 |
| 0.1245 | 27.0 | 44 | 0.4501 | 1.0 |
| 0.1245 | 28.0 | 46 | 0.4462 | 1.0 |
| 0.1245 | 29.0 | 47 | 0.4444 | 1.0 |
| 0.1245 | 30.0 | 49 | 0.4415 | 1.0 |
| 0.0996 | 31.0 | 51 | 0.4390 | 1.0 |
| 0.0996 | 32.0 | 52 | 0.4378 | 1.0 |
| 0.0996 | 33.0 | 53 | 0.4368 | 1.0 |
| 0.0996 | 34.0 | 55 | 0.4349 | 1.0 |
| 0.0996 | 35.0 | 57 | 0.4333 | 1.0 |
| 0.0996 | 36.0 | 58 | 0.4326 | 1.0 |
| 0.0862 | 37.0 | 60 | 0.4315 | 1.0 |
| 0.0862 | 38.0 | 62 | 0.4306 | 1.0 |
| 0.0862 | 39.0 | 63 | 0.4301 | 1.0 |
| 0.0862 | 40.0 | 65 | 0.4294 | 1.0 |
| 0.0862 | 41.0 | 67 | 0.4288 | 1.0 |
| 0.0862 | 42.0 | 68 | 0.4285 | 1.0 |
| 0.0765 | 43.0 | 70 | 0.4281 | 1.0 |
| 0.0765 | 44.0 | 72 | 0.4276 | 1.0 |
| 0.0765 | 45.0 | 73 | 0.4272 | 1.0 |
| 0.0765 | 46.0 | 75 | 0.4265 | 1.0 |
| 0.0765 | 47.0 | 77 | 0.4261 | 1.0 |
| 0.0765 | 48.0 | 78 | 0.4259 | 1.0 |
| 0.0765 | 49.0 | 79 | 0.4257 | 1.0 |
| 0.0783 | 50.0 | 81 | 0.4253 | 1.0 |
| 0.0783 | 51.0 | 83 | 0.4250 | 1.0 |
| 0.0783 | 52.0 | 84 | 0.4249 | 1.0 |
| 0.0783 | 53.0 | 86 | 0.4247 | 1.0 |
| 0.0783 | 54.0 | 88 | 0.4246 | 1.0 |
| 0.0783 | 55.0 | 89 | 0.4245 | 1.0 |
| 0.0652 | 56.0 | 91 | 0.4244 | 1.0 |
| 0.0652 | 57.0 | 93 | 0.4243 | 1.0 |
| 0.0652 | 58.0 | 94 | 0.4243 | 1.0 |
| 0.0652 | 59.0 | 96 | 0.4242 | 1.0 |
| 0.0652 | 60.0 | 98 | 0.4242 | 1.0 |
| 0.0652 | 61.0 | 99 | 0.4242 | 1.0 |
| 0.0655 | 61.09 | 100 | 0.4242 | 1.0 |
Framework versions
- Transformers 4.39.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2