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# Variome_5e-05_30_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: 0.0631
- Precision: 0.5610
- Recall: 0.5068
- F1: 0.5325
- Accuracy: 0.9859

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6582        | 0.51  | 25   | 0.1765          | 0.0       | 0.0    | 0.0    | 0.9769   |
| 0.1545        | 1.02  | 50   | 0.1746          | 0.0       | 0.0    | 0.0    | 0.9769   |
| 0.1544        | 1.53  | 75   | 0.1770          | 0.0       | 0.0    | 0.0    | 0.9769   |
| 0.1608        | 2.04  | 100  | 0.1752          | 0.0       | 0.0    | 0.0    | 0.9769   |
| 0.1552        | 2.55  | 125  | 0.1726          | 0.0       | 0.0    | 0.0    | 0.9769   |
| 0.1591        | 3.06  | 150  | 0.1582          | 0.0       | 0.0    | 0.0    | 0.9769   |
| 0.1185        | 3.57  | 175  | 0.1142          | 0.2978    | 0.0703 | 0.1138 | 0.9778   |
| 0.0979        | 4.08  | 200  | 0.1046          | 0.2865    | 0.1584 | 0.2041 | 0.9792   |
| 0.0889        | 4.59  | 225  | 0.0923          | 0.3965    | 0.2151 | 0.2789 | 0.9811   |
| 0.0749        | 5.1   | 250  | 0.0819          | 0.4126    | 0.3295 | 0.3664 | 0.9827   |
| 0.0622        | 5.61  | 275  | 0.0756          | 0.4497    | 0.3987 | 0.4227 | 0.9838   |
| 0.0635        | 6.12  | 300  | 0.0699          | 0.4970    | 0.4355 | 0.4642 | 0.9850   |
| 0.048         | 6.63  | 325  | 0.0672          | 0.5225    | 0.4512 | 0.4842 | 0.9852   |
| 0.0486        | 7.14  | 350  | 0.0663          | 0.5457    | 0.4827 | 0.5122 | 0.9852   |
| 0.0464        | 7.65  | 375  | 0.0666          | 0.5623    | 0.4879 | 0.5225 | 0.9856   |
| 0.043         | 8.16  | 400  | 0.0636          | 0.5464    | 0.5005 | 0.5225 | 0.9857   |
| 0.0393        | 8.67  | 425  | 0.0636          | 0.5693    | 0.4869 | 0.5249 | 0.9860   |
| 0.036         | 9.18  | 450  | 0.0636          | 0.5641    | 0.4942 | 0.5268 | 0.9858   |
| 0.0373        | 9.69  | 475  | 0.0637          | 0.5735    | 0.5037 | 0.5363 | 0.9860   |
| 0.0382        | 10.2  | 500  | 0.0631          | 0.5610    | 0.5068 | 0.5325 | 0.9859   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Variome_5e-05_30_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\_30\_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: 0.0631
* Precision: 0.5610
* Recall: 0.5068
* F1: 0.5325
* Accuracy: 0.9859


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 2.11.0
* 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 2.11.0
* 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 2.11.0
* 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 2.11.0
* Tokenizers 0.13.2