
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Variome_0.0001_0404_ES6

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: 0.0656
- Precision: 0.6474
- Recall: 0.5691
- F1: 0.6057
- Accuracy: 0.9854

## 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4714        | 0.13  | 25   | 0.1791          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1531        | 0.26  | 50   | 0.1271          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1288        | 0.39  | 75   | 0.1253          | 0.2032    | 0.0864 | 0.1212 | 0.9770   |
| 0.1253        | 0.52  | 100  | 0.1143          | 0.425     | 0.0163 | 0.0314 | 0.9762   |
| 0.101         | 0.65  | 125  | 0.1124          | 0.3438    | 0.1574 | 0.2159 | 0.9780   |
| 0.1062        | 0.79  | 150  | 0.1014          | 0.3569    | 0.2860 | 0.3175 | 0.9793   |
| 0.092         | 0.92  | 175  | 0.0986          | 0.4274    | 0.3081 | 0.3581 | 0.9805   |
| 0.0702        | 1.05  | 200  | 0.0942          | 0.5349    | 0.3234 | 0.4031 | 0.9810   |
| 0.0881        | 1.18  | 225  | 0.1009          | 0.3379    | 0.3580 | 0.3476 | 0.9765   |
| 0.0866        | 1.31  | 250  | 0.0780          | 0.5453    | 0.4155 | 0.4717 | 0.9830   |
| 0.0751        | 1.44  | 275  | 0.0749          | 0.5157    | 0.5038 | 0.5097 | 0.9830   |
| 0.0679        | 1.57  | 300  | 0.0732          | 0.5401    | 0.5432 | 0.5416 | 0.9835   |
| 0.0625        | 1.7   | 325  | 0.0728          | 0.6411    | 0.4491 | 0.5282 | 0.9842   |
| 0.065         | 1.83  | 350  | 0.0737          | 0.6115    | 0.4683 | 0.5304 | 0.9839   |
| 0.051         | 1.96  | 375  | 0.0720          | 0.5928    | 0.5240 | 0.5563 | 0.9840   |
| 0.0503        | 2.09  | 400  | 0.0669          | 0.6124    | 0.5489 | 0.5789 | 0.9854   |
| 0.0464        | 2.23  | 425  | 0.0718          | 0.625     | 0.5278 | 0.5723 | 0.9847   |
| 0.0528        | 2.36  | 450  | 0.0763          | 0.6628    | 0.4395 | 0.5286 | 0.9838   |
| 0.0473        | 2.49  | 475  | 0.0706          | 0.6109    | 0.5393 | 0.5729 | 0.9848   |
| 0.0542        | 2.62  | 500  | 0.0657          | 0.6159    | 0.5432 | 0.5773 | 0.9845   |
| 0.0508        | 2.75  | 525  | 0.0686          | 0.6515    | 0.5653 | 0.6053 | 0.9848   |
| 0.0476        | 2.88  | 550  | 0.0631          | 0.6857    | 0.5528 | 0.6121 | 0.9860   |
| 0.049         | 3.01  | 575  | 0.0689          | 0.6008    | 0.5720 | 0.5860 | 0.9842   |
| 0.0378        | 3.14  | 600  | 0.0720          | 0.6674    | 0.5605 | 0.6093 | 0.9854   |
| 0.0417        | 3.27  | 625  | 0.0695          | 0.6248    | 0.6104 | 0.6175 | 0.9852   |
| 0.0306        | 3.4   | 650  | 0.0723          | 0.6780    | 0.5537 | 0.6096 | 0.9860   |
| 0.0341        | 3.53  | 675  | 0.0664          | 0.6651    | 0.5547 | 0.6049 | 0.9862   |
| 0.0392        | 3.66  | 700  | 0.0656          | 0.6474    | 0.5691 | 0.6057 | 0.9854   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- 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_0404_ES6", "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\_0404\_ES6
==========================


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: 0.0656
* Precision: 0.6474
* Recall: 0.5691
* F1: 0.6057
* Accuracy: 0.9854


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: 2000


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* 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: 2000 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* 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: 2000 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* 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: 2000### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.2