File size: 5,651 Bytes
6bdfcab 6e34384 6bdfcab 6e34384 6bdfcab 6e34384 6bdfcab 4b9c9d6 6e34384 6bdfcab 8cc4fc1 6bdfcab 6e34384 8cc4fc1 6bdfcab b65d966 6bdfcab 6e34384 4b9c9d6 6bdfcab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
---
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: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# log_sage_reward_model
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.
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
|