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# roberta-base-finetuned-scientific-eval

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: 0.1154
- Precision: 0.7165
- Recall: 0.8063
- F1: 0.7587
- Accuracy: 0.9626

## 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   | 71   | 0.2567          | 0.3762    | 0.3333 | 0.3535 | 0.9314   |
| No log        | 2.0   | 142  | 0.1597          | 0.6677    | 0.6011 | 0.6327 | 0.9529   |
| No log        | 3.0   | 213  | 0.1222          | 0.6995    | 0.7293 | 0.7141 | 0.9622   |
| No log        | 4.0   | 284  | 0.1154          | 0.7165    | 0.8063 | 0.7587 | 0.9626   |
| No log        | 5.0   | 355  | 0.1310          | 0.6988    | 0.8063 | 0.7487 | 0.9651   |
| No log        | 6.0   | 426  | 0.1366          | 0.7101    | 0.8234 | 0.7625 | 0.9641   |
| No log        | 7.0   | 497  | 0.1453          | 0.7125    | 0.8262 | 0.7652 | 0.9632   |
| 0.1453        | 8.0   | 568  | 0.1388          | 0.7612    | 0.8262 | 0.7923 | 0.9672   |
| 0.1453        | 9.0   | 639  | 0.1455          | 0.7494    | 0.8433 | 0.7936 | 0.9669   |
| 0.1453        | 10.0  | 710  | 0.1441          | 0.745     | 0.8490 | 0.7936 | 0.9682   |


### Framework versions

- Transformers 4.27.2
- Pytorch 2.0.1+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-eval", "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-eval
======================================


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


* Loss: 0.1154
* Precision: 0.7165
* Recall: 0.8063
* F1: 0.7587
* Accuracy: 0.9626


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 4.27.2
* Pytorch 2.0.1+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 4.27.2
* Pytorch 2.0.1+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 4.27.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3