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# Variome_0.0005_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: 0.1812
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9760

## 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: 0.0005
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| 0.3356        | 0.35  | 25   | 0.1809          | 0.0       | 0.0    | 0.0 | 0.9760   |
| 0.1851        | 0.69  | 50   | 0.1807          | 0.0       | 0.0    | 0.0 | 0.9760   |
| 0.1635        | 1.04  | 75   | 0.1863          | 0.0       | 0.0    | 0.0 | 0.9760   |
| 0.1848        | 1.39  | 100  | 0.1810          | 0.0       | 0.0    | 0.0 | 0.9760   |
| 0.1697        | 1.74  | 125  | 0.1817          | 0.0       | 0.0    | 0.0 | 0.9760   |
| 0.1735        | 2.08  | 150  | 0.1802          | 0.0       | 0.0    | 0.0 | 0.9760   |
| 0.1576        | 2.43  | 175  | 0.1833          | 0.0       | 0.0    | 0.0 | 0.9760   |
| 0.178         | 2.78  | 200  | 0.1811          | 0.0       | 0.0    | 0.0 | 0.9760   |
| 0.18          | 3.12  | 225  | 0.1815          | 0.0       | 0.0    | 0.0 | 0.9760   |
| 0.1809        | 3.47  | 250  | 0.1825          | 0.0       | 0.0    | 0.0 | 0.9760   |
| 0.1616        | 3.82  | 275  | 0.1828          | 0.0       | 0.0    | 0.0 | 0.9760   |
| 0.1682        | 4.17  | 300  | 0.1812          | 0.0       | 0.0    | 0.0 | 0.9760   |


### 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_0.0005_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\_0.0005\_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: 0.1812
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accuracy: 0.9760


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: 0.0005
* 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: 0.0005
* 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: 0.0005
* 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: 0.0005
* 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