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# roberta-large-finetuned-csqa

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the commonsense_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9146
- Accuracy: 0.7330

## 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: 1e-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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3903        | 1.0   | 609  | 0.8845          | 0.6642   |
| 0.8939        | 2.0   | 1218 | 0.7054          | 0.7281   |
| 0.6163        | 3.0   | 1827 | 0.7452          | 0.7314   |
| 0.4245        | 4.0   | 2436 | 0.8369          | 0.7355   |
| 0.3258        | 5.0   | 3045 | 0.9146          | 0.7330   |


### Framework versions

- Transformers 4.9.0
- Pytorch 1.9.0
- Datasets 1.10.2
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["commonsense_qa"], "metrics": ["accuracy"], "model_index": [{"name": "roberta-large-finetuned-csqa", "results": [{"dataset": {"name": "commonsense_qa", "type": "commonsense_qa", "args": "default"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.7330057621002197}}]}]}
transformers pytorch roberta multiple-choice generated_from_trainer dataset:commonsense_qa license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #multiple-choice #generated_from_trainer #dataset-commonsense_qa #license-mit #endpoints_compatible #region-us 

roberta-large-finetuned-csqa
============================


This model is a fine-tuned version of roberta-large on the commonsense\_qa dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9146
* Accuracy: 0.7330


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: 1e-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
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.9.0
* Pytorch 1.9.0
* Datasets 1.10.2
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.9.0
* Pytorch 1.9.0
* Datasets 1.10.2
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #roberta #multiple-choice #generated_from_trainer #dataset-commonsense_qa #license-mit #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.9.0
* Pytorch 1.9.0
* Datasets 1.10.2
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #roberta #multiple-choice #generated_from_trainer #dataset-commonsense_qa #license-mit #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.9.0
* Pytorch 1.9.0
* Datasets 1.10.2
* Tokenizers 0.10.3