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# Variome_0.0001_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: 0.0638
- Precision: 0.6586
- Recall: 0.5816
- F1: 0.6177
- Accuracy: 0.9867

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3778        | 0.35  | 25   | 0.1802          | 0.0       | 0.0    | 0.0    | 0.9760   |
| 0.1563        | 0.69  | 50   | 0.1200          | 0.4524    | 0.0162 | 0.0313 | 0.9763   |
| 0.1061        | 1.04  | 75   | 0.1041          | 0.3604    | 0.2767 | 0.3130 | 0.9799   |
| 0.0981        | 1.39  | 100  | 0.0902          | 0.4585    | 0.3826 | 0.4171 | 0.9814   |
| 0.0807        | 1.74  | 125  | 0.0783          | 0.5129    | 0.4244 | 0.4645 | 0.9835   |
| 0.0731        | 2.08  | 150  | 0.0727          | 0.5513    | 0.5047 | 0.5270 | 0.9844   |
| 0.0526        | 2.43  | 175  | 0.0720          | 0.6368    | 0.5167 | 0.5705 | 0.9856   |
| 0.0604        | 2.78  | 200  | 0.0686          | 0.589     | 0.5030 | 0.5426 | 0.9849   |
| 0.0542        | 3.12  | 225  | 0.0671          | 0.6131    | 0.5371 | 0.5726 | 0.9856   |
| 0.0441        | 3.47  | 250  | 0.0669          | 0.6635    | 0.5389 | 0.5947 | 0.9860   |
| 0.0438        | 3.82  | 275  | 0.0667          | 0.625     | 0.5423 | 0.5807 | 0.9859   |
| 0.0381        | 4.17  | 300  | 0.0658          | 0.6562    | 0.5525 | 0.5999 | 0.9858   |
| 0.0404        | 4.51  | 325  | 0.0648          | 0.6578    | 0.5713 | 0.6115 | 0.9862   |
| 0.0341        | 4.86  | 350  | 0.0625          | 0.6637    | 0.5679 | 0.6121 | 0.9865   |
| 0.0298        | 5.21  | 375  | 0.0646          | 0.6727    | 0.5739 | 0.6194 | 0.9868   |
| 0.029         | 5.56  | 400  | 0.0643          | 0.6569    | 0.5739 | 0.6126 | 0.9861   |
| 0.0287        | 5.9   | 425  | 0.0637          | 0.6713    | 0.5739 | 0.6188 | 0.9869   |
| 0.027         | 6.25  | 450  | 0.0637          | 0.6660    | 0.5739 | 0.6165 | 0.9868   |
| 0.0236        | 6.6   | 475  | 0.0639          | 0.6644    | 0.5833 | 0.6212 | 0.9869   |
| 0.0233        | 6.94  | 500  | 0.0638          | 0.6586    | 0.5816 | 0.6177 | 0.9867   |


### 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_0.0001_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\_0.0001\_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: 0.0638
* Precision: 0.6586
* Recall: 0.5816
* F1: 0.6177
* Accuracy: 0.9867


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: 0.0001
* 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: 0.0001
* 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: 0.0001
* 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: 0.0001
* 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