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# Variome_2e-05_250

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: 0.0798
- Precision: 0.4740
- Recall: 0.4133
- F1: 0.4416
- Accuracy: 0.9830

## 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
- training_steps: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.052         | 0.35  | 25   | 0.1874          | 0.0       | 0.0    | 0.0    | 0.9760   |
| 0.1879        | 0.69  | 50   | 0.1794          | 0.0       | 0.0    | 0.0    | 0.9760   |
| 0.1625        | 1.04  | 75   | 0.1736          | 0.0       | 0.0    | 0.0    | 0.9760   |
| 0.1643        | 1.39  | 100  | 0.1323          | 0.0       | 0.0    | 0.0    | 0.9760   |
| 0.1228        | 1.74  | 125  | 0.1183          | 0.2137    | 0.0854 | 0.1220 | 0.9769   |
| 0.1165        | 2.08  | 150  | 0.1113          | 0.2017    | 0.1230 | 0.1528 | 0.9774   |
| 0.0989        | 2.43  | 175  | 0.1072          | 0.3520    | 0.2092 | 0.2625 | 0.9792   |
| 0.1057        | 2.78  | 200  | 0.1008          | 0.3322    | 0.2528 | 0.2871 | 0.9795   |
| 0.0997        | 3.12  | 225  | 0.0961          | 0.3952    | 0.2801 | 0.3278 | 0.9804   |
| 0.0895        | 3.47  | 250  | 0.0930          | 0.4115    | 0.2938 | 0.3428 | 0.9807   |
| 0.0813        | 3.82  | 275  | 0.0904          | 0.3897    | 0.3305 | 0.3577 | 0.9810   |
| 0.0767        | 4.17  | 300  | 0.0885          | 0.4294    | 0.3348 | 0.3762 | 0.9815   |
| 0.0763        | 4.51  | 325  | 0.0851          | 0.4277    | 0.3715 | 0.3976 | 0.9817   |
| 0.0714        | 4.86  | 350  | 0.0836          | 0.4361    | 0.3698 | 0.4002 | 0.9822   |
| 0.0714        | 5.21  | 375  | 0.0825          | 0.4862    | 0.3766 | 0.4244 | 0.9828   |
| 0.0678        | 5.56  | 400  | 0.0814          | 0.4684    | 0.3920 | 0.4268 | 0.9828   |
| 0.0674        | 5.9   | 425  | 0.0802          | 0.4638    | 0.3988 | 0.4288 | 0.9830   |
| 0.0688        | 6.25  | 450  | 0.0792          | 0.4672    | 0.4073 | 0.4352 | 0.9828   |
| 0.0646        | 6.6   | 475  | 0.0802          | 0.4847    | 0.4056 | 0.4417 | 0.9831   |
| 0.0607        | 6.94  | 500  | 0.0798          | 0.4740    | 0.4133 | 0.4416 | 0.9830   |


### 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_2e-05_250", "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\_2e-05\_250
===================


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: 0.0798
* Precision: 0.4740
* Recall: 0.4133
* F1: 0.4416
* Accuracy: 0.9830


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


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