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# test_trainer

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7153
- Accuracy: 0.501

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6412        | 0.01  | 1    | 0.7288          | 0.501    |
| 0.6171        | 0.02  | 2    | 0.7083          | 0.501    |
| 0.5805        | 0.02  | 3    | 0.7153          | 0.501    |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["rotten_tomatoes"], "metrics": ["accuracy"], "model-index": [{"name": "test_trainer", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "rotten_tomatoes", "type": "rotten_tomatoes", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.501, "name": "Accuracy"}]}]}]}
transformers pytorch bert text-classification generated_from_trainer dataset:rotten_tomatoes license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-rotten_tomatoes #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

test\_trainer
=============


This model is a fine-tuned version of bert-base-cased on the rotten\_tomatoes dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7153
* Accuracy: 0.501


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 3


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 3 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-rotten_tomatoes #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 3 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3