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# Variome_2e-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: 0.0928
- Precision: 0.5437
- Recall: 0.4211
- F1: 0.4746
- Accuracy: 0.9852

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.1552        | 5.0   | 25   | 0.1636          | 0.0       | 0.0    | 0.0    | 0.9794   |
| 0.1671        | 10.0  | 50   | 0.1345          | 0.0       | 0.0    | 0.0    | 0.9794   |
| 0.1297        | 15.0  | 75   | 0.1076          | 0.2683    | 0.0827 | 0.1264 | 0.9806   |
| 0.0995        | 20.0  | 100  | 0.1047          | 0.24      | 0.1353 | 0.1731 | 0.9810   |
| 0.0845        | 25.0  | 125  | 0.0987          | 0.2289    | 0.1429 | 0.1759 | 0.9813   |
| 0.0722        | 30.0  | 150  | 0.1001          | 0.2558    | 0.1654 | 0.2009 | 0.9816   |
| 0.0642        | 35.0  | 175  | 0.0994          | 0.3117    | 0.1805 | 0.2286 | 0.9821   |
| 0.0564        | 40.0  | 200  | 0.0938          | 0.3204    | 0.2481 | 0.2797 | 0.9817   |
| 0.0481        | 45.0  | 225  | 0.0935          | 0.4070    | 0.2632 | 0.3196 | 0.9833   |
| 0.0416        | 50.0  | 250  | 0.0913          | 0.4167    | 0.3383 | 0.3734 | 0.9836   |
| 0.0363        | 55.0  | 275  | 0.0911          | 0.4653    | 0.3534 | 0.4017 | 0.9847   |
| 0.0321        | 60.0  | 300  | 0.0909          | 0.4495    | 0.3684 | 0.4050 | 0.9842   |
| 0.0293        | 65.0  | 325  | 0.0918          | 0.5361    | 0.3910 | 0.4522 | 0.9852   |
| 0.0269        | 70.0  | 350  | 0.0936          | 0.5444    | 0.3684 | 0.4395 | 0.9853   |
| 0.0251        | 75.0  | 375  | 0.0936          | 0.5833    | 0.4211 | 0.4891 | 0.9858   |
| 0.0242        | 80.0  | 400  | 0.0920          | 0.5534    | 0.4286 | 0.4831 | 0.9854   |
| 0.0232        | 85.0  | 425  | 0.0928          | 0.5612    | 0.4135 | 0.4762 | 0.9855   |
| 0.0216        | 90.0  | 450  | 0.0928          | 0.5437    | 0.4211 | 0.4746 | 0.9852   |


### 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_2e-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\_2e-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: 0.0928
* Precision: 0.5437
* Recall: 0.4211
* F1: 0.4746
* Accuracy: 0.9852


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 2.10.1
* 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 2.10.1
* 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: 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 2.10.1
* 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: 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 2.10.1
* Tokenizers 0.13.2