
<!-- 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. -->

# roberta-base-finetuned-sdg

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4993
- Acc: 0.9024

## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Acc    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4679        | 1.0   | 254  | 0.3660          | 0.8963 |
| 0.3578        | 2.0   | 508  | 0.3689          | 0.9019 |
| 0.2739        | 3.0   | 762  | 0.3284          | 0.9035 |
| 0.1841        | 4.0   | 1016 | 0.3763          | 0.9019 |
| 0.1127        | 5.0   | 1270 | 0.4174          | 0.9024 |
| 0.0822        | 6.0   | 1524 | 0.4523          | 0.9013 |
| 0.0329        | 7.0   | 1778 | 0.4829          | 0.9030 |
| 0.0157        | 8.0   | 2032 | 0.4993          | 0.9024 |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0a0+8a1a93a
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-finetuned-sdg", "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-base-finetuned-sdg
==========================


This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4993
* Acc: 0.9024


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: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0a0+8a1a93a
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0a0+8a1a93a
* Datasets 2.4.0
* Tokenizers 0.12.1
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: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0a0+8a1a93a
* Datasets 2.4.0
* Tokenizers 0.12.1
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: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0a0+8a1a93a
* Datasets 2.4.0
* Tokenizers 0.12.1