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# roberta-base-finetuned-scientific-exp

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: 0.1255
- Precision: 0.7662
- Recall: 0.7484
- F1: 0.7572
- Accuracy: 0.9674

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 81   | 0.2172          | 0.6049    | 0.5180 | 0.5581 | 0.9433   |
| No log        | 2.0   | 162  | 0.1470          | 0.7556    | 0.6469 | 0.6970 | 0.9582   |
| No log        | 3.0   | 243  | 0.1255          | 0.7662    | 0.7484 | 0.7572 | 0.9674   |
| No log        | 4.0   | 324  | 0.1261          | 0.7546    | 0.7738 | 0.7641 | 0.9666   |
| No log        | 5.0   | 405  | 0.1339          | 0.7184    | 0.8414 | 0.7751 | 0.9635   |
| No log        | 6.0   | 486  | 0.1350          | 0.7112    | 0.8330 | 0.7673 | 0.9627   |
| 0.1498        | 7.0   | 567  | 0.1362          | 0.7471    | 0.8309 | 0.7868 | 0.9693   |
| 0.1498        | 8.0   | 648  | 0.1530          | 0.7174    | 0.8266 | 0.7682 | 0.9644   |
| 0.1498        | 9.0   | 729  | 0.1587          | 0.7392    | 0.8330 | 0.7833 | 0.9655   |
| 0.1498        | 10.0  | 810  | 0.1610          | 0.7416    | 0.8372 | 0.7865 | 0.9651   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-base-finetuned-scientific-exp", "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-scientific-exp
=====================================


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


* Loss: 0.1255
* Precision: 0.7662
* Recall: 0.7484
* F1: 0.7572
* Accuracy: 0.9674


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: 10


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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: 10 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
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: 10 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3