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# Variome_0.0001_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: 0.1367
- Precision: 0.6567
- Recall: 0.3308
- F1: 0.44
- Accuracy: 0.9842

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4296        | 5.0   | 25   | 0.1570          | 0.0       | 0.0    | 0.0    | 0.9794   |
| 0.1679        | 10.0  | 50   | 0.1546          | 0.0       | 0.0    | 0.0    | 0.9794   |
| 0.1632        | 15.0  | 75   | 0.1375          | 0.0       | 0.0    | 0.0    | 0.9794   |
| 0.1332        | 20.0  | 100  | 0.1357          | 0.2381    | 0.0376 | 0.0649 | 0.9799   |
| 0.0886        | 25.0  | 125  | 0.1250          | 0.2222    | 0.0602 | 0.0947 | 0.9805   |
| 0.0714        | 30.0  | 150  | 0.1278          | 0.3333    | 0.1053 | 0.16   | 0.9809   |
| 0.0479        | 35.0  | 175  | 0.1220          | 0.5       | 0.2256 | 0.3109 | 0.9831   |
| 0.0301        | 40.0  | 200  | 0.1259          | 0.6154    | 0.3008 | 0.4040 | 0.9841   |
| 0.0198        | 45.0  | 225  | 0.1257          | 0.6364    | 0.3158 | 0.4221 | 0.9846   |
| 0.0138        | 50.0  | 250  | 0.1240          | 0.6184    | 0.3534 | 0.4498 | 0.9847   |
| 0.0099        | 55.0  | 275  | 0.1301          | 0.5823    | 0.3459 | 0.4340 | 0.9837   |
| 0.008         | 60.0  | 300  | 0.1343          | 0.5584    | 0.3233 | 0.4095 | 0.9832   |
| 0.0066        | 65.0  | 325  | 0.1290          | 0.5625    | 0.3383 | 0.4225 | 0.9830   |
| 0.0054        | 70.0  | 350  | 0.1366          | 0.6061    | 0.3008 | 0.4020 | 0.9838   |
| 0.0047        | 75.0  | 375  | 0.1334          | 0.6111    | 0.3308 | 0.4293 | 0.9841   |
| 0.0044        | 80.0  | 400  | 0.1367          | 0.6567    | 0.3308 | 0.44   | 0.9842   |


### 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_0.0001_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\_0.0001\_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: 0.1367
* Precision: 0.6567
* Recall: 0.3308
* F1: 0.44
* Accuracy: 0.9842


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