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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- poem_sentiment
metrics:
- accuracy
model-index:
- name: Bert_uncased_fine_tuned_Reward_Model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: poem_sentiment
type: poem_sentiment
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.875
---
<!-- 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. -->
# Bert_uncased_fine_tuned_Reward_Model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the poem_sentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0876
- Mse: 0.0876
- Mae: 0.1403
- R2: 0.7389
- Accuracy: 0.875
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:|
| No log | 1.0 | 53 | 0.1744 | 0.1744 | 0.2973 | 0.4805 | 0.7885 |
| No log | 2.0 | 106 | 0.1074 | 0.1074 | 0.2333 | 0.6801 | 0.8846 |
| No log | 3.0 | 159 | 0.1026 | 0.1026 | 0.2134 | 0.6943 | 0.8654 |
| No log | 4.0 | 212 | 0.0877 | 0.0877 | 0.1841 | 0.7388 | 0.8942 |
| No log | 5.0 | 265 | 0.1000 | 0.1000 | 0.2007 | 0.7021 | 0.8942 |
| No log | 6.0 | 318 | 0.0863 | 0.0863 | 0.1738 | 0.7429 | 0.8942 |
| No log | 7.0 | 371 | 0.0966 | 0.0966 | 0.1827 | 0.7122 | 0.8846 |
| No log | 8.0 | 424 | 0.0946 | 0.0946 | 0.1701 | 0.7183 | 0.8846 |
| No log | 9.0 | 477 | 0.0978 | 0.0978 | 0.1658 | 0.7088 | 0.875 |
| 0.0516 | 10.0 | 530 | 0.0854 | 0.0854 | 0.1639 | 0.7457 | 0.875 |
| 0.0516 | 11.0 | 583 | 0.0947 | 0.0947 | 0.1620 | 0.7181 | 0.8846 |
| 0.0516 | 12.0 | 636 | 0.0907 | 0.0907 | 0.1516 | 0.7297 | 0.8846 |
| 0.0516 | 13.0 | 689 | 0.0885 | 0.0885 | 0.1546 | 0.7364 | 0.875 |
| 0.0516 | 14.0 | 742 | 0.0849 | 0.0849 | 0.1452 | 0.7471 | 0.8942 |
| 0.0516 | 15.0 | 795 | 0.0823 | 0.0823 | 0.1428 | 0.7548 | 0.8846 |
| 0.0516 | 16.0 | 848 | 0.0864 | 0.0864 | 0.1429 | 0.7427 | 0.8846 |
| 0.0516 | 17.0 | 901 | 0.0854 | 0.0854 | 0.1427 | 0.7457 | 0.8846 |
| 0.0516 | 18.0 | 954 | 0.0860 | 0.0860 | 0.1429 | 0.7437 | 0.875 |
| 0.0059 | 19.0 | 1007 | 0.0871 | 0.0871 | 0.1438 | 0.7406 | 0.875 |
| 0.0059 | 20.0 | 1060 | 0.0876 | 0.0876 | 0.1403 | 0.7389 | 0.875 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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