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# roberta-scarcasm-discriminator

roberta-base

label0: unsarcasitic

label1: sarcastic
The fine tune method in my github https://github.com/yangyangxusheng/Fine-tune-use-transformers

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: 0.1844
- Accuracy: 0.9698

## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.144         | 1.0   | 2179 | 0.2522          | 0.9215   |
| 0.116         | 2.0   | 4358 | 0.2105          | 0.9530   |
| 0.0689        | 3.0   | 6537 | 0.2015          | 0.9610   |
| 0.028         | 4.0   | 8716 | 0.1844          | 0.9698   |


### Framework versions

- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-scarcasm-discriminator", "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-scarcasm-discriminator
==============================


roberta-base


label0: unsarcasitic


label1: sarcastic
The fine tune method in my github URL


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


* Loss: 0.1844
* Accuracy: 0.9698


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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 4


### Training results



### Framework versions


* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3



### 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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
passage: 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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 4### Training results### Framework versions


* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3