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# roberta-base-finetuned-papernew5

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: 0.0864
- Precision: 0.7835
- Recall: 0.8144
- F1: 0.7986
- Accuracy: 0.9742

## 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: 2e-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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 81   | 0.1872          | 0.6657    | 0.5174 | 0.5822 | 0.9511   |
| No log        | 2.0   | 162  | 0.1321          | 0.6189    | 0.7912 | 0.6945 | 0.9585   |
| No log        | 3.0   | 243  | 0.0864          | 0.7835    | 0.8144 | 0.7986 | 0.9742   |
| No log        | 4.0   | 324  | 0.0891          | 0.7532    | 0.8144 | 0.7826 | 0.9723   |
| No log        | 5.0   | 405  | 0.1004          | 0.7542    | 0.8399 | 0.7947 | 0.9723   |
| No log        | 6.0   | 486  | 0.1197          | 0.7267    | 0.8515 | 0.7842 | 0.9677   |
| 0.1476        | 7.0   | 567  | 0.1237          | 0.7605    | 0.8399 | 0.7982 | 0.9709   |
| 0.1476        | 8.0   | 648  | 0.1104          | 0.7383    | 0.8445 | 0.7879 | 0.9728   |
| 0.1476        | 9.0   | 729  | 0.1179          | 0.7863    | 0.8283 | 0.8068 | 0.9742   |
| 0.1476        | 10.0  | 810  | 0.1150          | 0.7811    | 0.8608 | 0.8190 | 0.9752   |
| 0.1476        | 11.0  | 891  | 0.1273          | 0.7602    | 0.8608 | 0.8074 | 0.9728   |
| 0.1476        | 12.0  | 972  | 0.1230          | 0.7711    | 0.8677 | 0.8166 | 0.9751   |
| 0.014         | 13.0  | 1053 | 0.1280          | 0.7815    | 0.8631 | 0.8203 | 0.9753   |
| 0.014         | 14.0  | 1134 | 0.1285          | 0.7755    | 0.8654 | 0.8180 | 0.9753   |
| 0.014         | 15.0  | 1215 | 0.1336          | 0.7639    | 0.8631 | 0.8105 | 0.9740   |


### 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-papernew5", "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-papernew5
================================


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


* Loss: 0.0864
* Precision: 0.7835
* Recall: 0.8144
* F1: 0.7986
* Accuracy: 0.9742


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: 2e-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: 15


### 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: 2e-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: 15 ### 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: 2e-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: 15 ### 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: 2e-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: 15### Training results### Framework versions


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