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# Variome_5e-05_29_03

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1054
- Precision: 0.5304
- Recall: 0.4586
- F1: 0.4919
- Accuracy: 0.9843

## 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
- training_steps: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5733        | 5.0   | 25   | 0.1581          | 0.0       | 0.0    | 0.0    | 0.9794   |
| 0.1685        | 10.0  | 50   | 0.1553          | 0.0       | 0.0    | 0.0    | 0.9794   |
| 0.1691        | 15.0  | 75   | 0.1547          | 0.0       | 0.0    | 0.0    | 0.9794   |
| 0.1606        | 20.0  | 100  | 0.1335          | 0.0       | 0.0    | 0.0    | 0.9794   |
| 0.1144        | 25.0  | 125  | 0.1044          | 0.2151    | 0.1504 | 0.1770 | 0.9802   |
| 0.0795        | 30.0  | 150  | 0.1023          | 0.2       | 0.1504 | 0.1717 | 0.9804   |
| 0.0612        | 35.0  | 175  | 0.1102          | 0.4118    | 0.2105 | 0.2786 | 0.9831   |
| 0.0465        | 40.0  | 200  | 0.0991          | 0.4158    | 0.3158 | 0.3590 | 0.9840   |
| 0.0352        | 45.0  | 225  | 0.0995          | 0.4653    | 0.3534 | 0.4017 | 0.9838   |
| 0.0281        | 50.0  | 250  | 0.0969          | 0.4685    | 0.3910 | 0.4262 | 0.9838   |
| 0.0223        | 55.0  | 275  | 0.0976          | 0.5684    | 0.4060 | 0.4737 | 0.9853   |
| 0.0183        | 60.0  | 300  | 0.0992          | 0.5093    | 0.4135 | 0.4564 | 0.9848   |
| 0.0154        | 65.0  | 325  | 0.0996          | 0.5816    | 0.4286 | 0.4935 | 0.9858   |
| 0.0131        | 70.0  | 350  | 0.1007          | 0.5221    | 0.4436 | 0.4797 | 0.9842   |
| 0.0109        | 75.0  | 375  | 0.1023          | 0.5130    | 0.4436 | 0.4758 | 0.9842   |
| 0.0094        | 80.0  | 400  | 0.1037          | 0.5566    | 0.4436 | 0.4937 | 0.9851   |
| 0.0085        | 85.0  | 425  | 0.1054          | 0.5304    | 0.4586 | 0.4919 | 0.9843   |


### Framework versions

- Transformers 4.27.4
- 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": "Variome_5e-05_29_03", "results": []}]}
transformers pytorch bert token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

Variome\_5e-05\_29\_03
======================


This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1054
* Precision: 0.5304
* Recall: 0.4586
* F1: 0.4919
* Accuracy: 0.9843


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
* training\_steps: 500


### Training results



### Framework versions


* Transformers 4.27.4
* 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
* training\_steps: 500 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #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
* training\_steps: 500 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #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
* training\_steps: 500### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2