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# Variome_5e-05_0404_ES6_strict_tok

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: 0.0683
- Precision: 0.6382
- Recall: 0.5029
- F1: 0.5625
- 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: 5e-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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5718        | 0.13  | 25   | 0.1871          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1825        | 0.26  | 50   | 0.1824          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1927        | 0.39  | 75   | 0.1846          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1807        | 0.52  | 100  | 0.1811          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1695        | 0.65  | 125  | 0.1861          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1782        | 0.79  | 150  | 0.1807          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.174         | 0.92  | 175  | 0.1798          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1547        | 1.05  | 200  | 0.1706          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1539        | 1.18  | 225  | 0.1402          | 0.0       | 0.0    | 0.0    | 0.9759   |
| 0.1415        | 1.31  | 250  | 0.1281          | 0.3333    | 0.0010 | 0.0019 | 0.9759   |
| 0.125         | 1.44  | 275  | 0.1154          | 0.1606    | 0.1104 | 0.1308 | 0.9772   |
| 0.1143        | 1.57  | 300  | 0.1043          | 0.2136    | 0.1689 | 0.1886 | 0.9774   |
| 0.1009        | 1.7   | 325  | 0.1014          | 0.2602    | 0.2207 | 0.2388 | 0.9784   |
| 0.0952        | 1.83  | 350  | 0.0918          | 0.3286    | 0.2418 | 0.2786 | 0.9798   |
| 0.0754        | 1.96  | 375  | 0.0908          | 0.5       | 0.2332 | 0.3181 | 0.9804   |
| 0.0759        | 2.09  | 400  | 0.0896          | 0.5473    | 0.3052 | 0.3919 | 0.9823   |
| 0.0676        | 2.23  | 425  | 0.0817          | 0.4222    | 0.3388 | 0.3759 | 0.9820   |
| 0.0788        | 2.36  | 450  | 0.0997          | 0.5549    | 0.1699 | 0.2601 | 0.9793   |
| 0.0692        | 2.49  | 475  | 0.0780          | 0.4634    | 0.4251 | 0.4434 | 0.9828   |
| 0.07          | 2.62  | 500  | 0.0734          | 0.5784    | 0.4002 | 0.4731 | 0.9835   |
| 0.0694        | 2.75  | 525  | 0.0693          | 0.5741    | 0.4722 | 0.5182 | 0.9850   |
| 0.0645        | 2.88  | 550  | 0.0677          | 0.6261    | 0.4741 | 0.5396 | 0.9850   |
| 0.0685        | 3.01  | 575  | 0.0698          | 0.5831    | 0.4511 | 0.5087 | 0.9847   |
| 0.0505        | 3.14  | 600  | 0.0683          | 0.6382    | 0.5029 | 0.5625 | 0.9852   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Variome_5e-05_0404_ES6_strict_tok", "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\_5e-05\_0404\_ES6\_strict\_tok
======================================


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: 0.0683
* Precision: 0.6382
* Recall: 0.5029
* F1: 0.5625
* 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: 5e-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: 2000


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 2000 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
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: 5e-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: 2000 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
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: 5e-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: 2000### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3