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# roberta-base-finetuned-academic

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the elsevier-oa-cc-by dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1158

## 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: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.1903        | 0.25  | 1025  | 2.0998          |
| 2.1752        | 0.5   | 2050  | 2.1186          |
| 2.1864        | 0.75  | 3075  | 2.1073          |
| 2.1874        | 1.0   | 4100  | 2.1177          |
| 2.1669        | 1.25  | 5125  | 2.1091          |
| 2.1859        | 1.5   | 6150  | 2.1212          |
| 2.1783        | 1.75  | 7175  | 2.1096          |
| 2.1734        | 2.0   | 8200  | 2.0998          |
| 2.1712        | 2.25  | 9225  | 2.0972          |
| 2.1812        | 2.5   | 10250 | 2.1051          |
| 2.1811        | 2.75  | 11275 | 2.1150          |
| 2.1826        | 3.0   | 12300 | 2.1097          |
| 2.172         | 3.25  | 13325 | 2.1115          |
| 2.1745        | 3.5   | 14350 | 2.1098          |
| 2.1758        | 3.75  | 15375 | 2.1101          |
| 2.1834        | 4.0   | 16400 | 2.1232          |
| 2.1836        | 4.25  | 17425 | 2.1052          |
| 2.1791        | 4.5   | 18450 | 2.1186          |
| 2.172         | 4.75  | 19475 | 2.1039          |
| 2.1797        | 5.0   | 20500 | 2.1015          |


### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["elsevier-oa-cc-by"], "model-index": [{"name": "roberta-base-finetuned-academic", "results": []}]}
transformers pytorch tensorboard roberta fill-mask generated_from_trainer dataset:elsevier-oa-cc-by license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #dataset-elsevier-oa-cc-by #license-mit #autotrain_compatible #endpoints_compatible #region-us 

roberta-base-finetuned-academic
===============================


This model is a fine-tuned version of roberta-base on the elsevier-oa-cc-by dataset.
It achieves the following results on the evaluation set:


* Loss: 2.1158


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: 32
* eval\_batch\_size: 32
* 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 4.19.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 32
* 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 4.19.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #dataset-elsevier-oa-cc-by #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 32
* 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 4.19.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #dataset-elsevier-oa-cc-by #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 32
* 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 4.19.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1