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# roberta-base-finetuned-paper

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: 0.1620
- Precision: 0.7605
- Recall: 0.8141
- F1: 0.7864
- Accuracy: 0.9765

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 73   | 0.1575          | 0.6484    | 0.5673 | 0.6051 | 0.9591   |
| No log        | 2.0   | 146  | 0.0964          | 0.6723    | 0.7628 | 0.7147 | 0.9718   |
| No log        | 3.0   | 219  | 0.1233          | 0.6447    | 0.7853 | 0.7081 | 0.9655   |
| No log        | 4.0   | 292  | 0.1153          | 0.7563    | 0.7660 | 0.7611 | 0.9737   |
| No log        | 5.0   | 365  | 0.1194          | 0.7265    | 0.8173 | 0.7692 | 0.9727   |
| No log        | 6.0   | 438  | 0.1243          | 0.7286    | 0.8173 | 0.7704 | 0.9722   |
| 0.0974        | 7.0   | 511  | 0.1406          | 0.7202    | 0.7756 | 0.7469 | 0.9732   |
| 0.0974        | 8.0   | 584  | 0.1436          | 0.7406    | 0.7596 | 0.7500 | 0.9706   |
| 0.0974        | 9.0   | 657  | 0.1687          | 0.7524    | 0.7596 | 0.7560 | 0.9738   |
| 0.0974        | 10.0  | 730  | 0.1591          | 0.7394    | 0.7821 | 0.7601 | 0.9743   |
| 0.0974        | 11.0  | 803  | 0.1431          | 0.7619    | 0.8205 | 0.7901 | 0.9754   |
| 0.0974        | 12.0  | 876  | 0.1487          | 0.7477    | 0.7981 | 0.7721 | 0.9745   |
| 0.0974        | 13.0  | 949  | 0.1512          | 0.7764    | 0.8013 | 0.7886 | 0.9763   |
| 0.0043        | 14.0  | 1022 | 0.1532          | 0.7645    | 0.8013 | 0.7825 | 0.9754   |
| 0.0043        | 15.0  | 1095 | 0.1531          | 0.7720    | 0.8141 | 0.7925 | 0.9761   |
| 0.0043        | 16.0  | 1168 | 0.1590          | 0.7635    | 0.8173 | 0.7895 | 0.9756   |
| 0.0043        | 17.0  | 1241 | 0.1615          | 0.7559    | 0.8237 | 0.7883 | 0.9754   |
| 0.0043        | 18.0  | 1314 | 0.1624          | 0.7612    | 0.8173 | 0.7883 | 0.9759   |
| 0.0043        | 19.0  | 1387 | 0.1622          | 0.7574    | 0.8205 | 0.7877 | 0.9763   |
| 0.0043        | 20.0  | 1460 | 0.1620          | 0.7605    | 0.8141 | 0.7864 | 0.9765   |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-finetuned-paper", "results": []}]}
transformers pytorch tensorboard roberta token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

roberta-base-finetuned-paper
============================


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


* Loss: 0.1620
* Precision: 0.7605
* Recall: 0.8141
* F1: 0.7864
* Accuracy: 0.9765


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: 20


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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: 20 ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #token-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: 20 ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #token-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: 20### Training results### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2