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# roberta-similarity

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7067
- Accuracy: 0.832

## 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: 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6376        | 0.16  | 10   | 0.6287          | 0.672    |
| 0.5909        | 0.32  | 20   | 0.5762          | 0.672    |
| 0.5422        | 0.48  | 30   | 0.6498          | 0.672    |
| 0.5876        | 0.63  | 40   | 0.6411          | 0.672    |
| 0.523         | 0.79  | 50   | 0.7330          | 0.67     |
| 0.5686        | 0.95  | 60   | 0.6911          | 0.672    |
| 0.4743        | 1.11  | 70   | 0.5254          | 0.792    |
| 0.4183        | 1.27  | 80   | 0.4998          | 0.818    |
| 0.3682        | 1.43  | 90   | 0.5912          | 0.816    |
| 0.6203        | 1.59  | 100  | 0.9526          | 0.706    |
| 0.5078        | 1.75  | 110  | 0.5348          | 0.824    |
| 0.3214        | 1.9   | 120  | 0.5120          | 0.816    |
| 0.3352        | 2.06  | 130  | 0.5275          | 0.808    |
| 0.2805        | 2.22  | 140  | 0.5597          | 0.816    |
| 0.2541        | 2.38  | 150  | 0.5253          | 0.83     |
| 0.3769        | 2.54  | 160  | 0.5075          | 0.796    |
| 0.3203        | 2.7   | 170  | 0.4701          | 0.816    |
| 0.2153        | 2.86  | 180  | 0.5483          | 0.814    |
| 0.1822        | 3.02  | 190  | 0.5819          | 0.832    |
| 0.1761        | 3.17  | 200  | 0.6913          | 0.822    |
| 0.301         | 3.33  | 210  | 0.7678          | 0.804    |
| 0.21          | 3.49  | 220  | 0.9464          | 0.798    |
| 0.3224        | 3.65  | 230  | 0.6209          | 0.832    |
| 0.133         | 3.81  | 240  | 0.7540          | 0.818    |
| 0.1826        | 3.97  | 250  | 0.7332          | 0.828    |
| 0.2547        | 4.13  | 260  | 0.6782          | 0.83     |
| 0.1321        | 4.29  | 270  | 0.7430          | 0.824    |
| 0.1661        | 4.44  | 280  | 0.8056          | 0.826    |
| 0.1525        | 4.6   | 290  | 0.6864          | 0.828    |
| 0.2085        | 4.76  | 300  | 0.6900          | 0.832    |
| 0.1201        | 4.92  | 310  | 0.7067          | 0.832    |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-similarity", "results": []}]}
transformers pytorch roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

roberta-similarity
==================


This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7067
* Accuracy: 0.832


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: 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: 5


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 5 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 5 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 5### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3