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# 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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["poem_sentiment"], "metrics": ["accuracy"], "model-index": [{"name": "Bert_uncased_fine_tuned_Reward_Model", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "poem_sentiment", "type": "poem_sentiment", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.875, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:poem_sentiment license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-poem_sentiment #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

Bert\_uncased\_fine\_tuned\_Reward\_Model
=========================================


This model is a fine-tuned version of 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



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-poem_sentiment #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-poem_sentiment #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2