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# SETH_5e-5_0.03_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: 0.0720
- Precision: 0.7492
- Recall: 0.8176
- F1: 0.7819
- Accuracy: 0.9828

## 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: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3766        | 0.96  | 25   | 0.1685          | 0.0       | 0.0    | 0.0    | 0.9583   |
| 0.1045        | 1.92  | 50   | 0.0804          | 0.4570    | 0.7143 | 0.5574 | 0.9703   |
| 0.062         | 2.88  | 75   | 0.0667          | 0.5704    | 0.8090 | 0.6690 | 0.9756   |
| 0.0476        | 3.85  | 100  | 0.0604          | 0.6716    | 0.8520 | 0.7511 | 0.9797   |
| 0.0394        | 4.81  | 125  | 0.0667          | 0.6328    | 0.8571 | 0.7281 | 0.9765   |
| 0.0297        | 5.77  | 150  | 0.0602          | 0.6823    | 0.8279 | 0.7481 | 0.9802   |
| 0.0263        | 6.73  | 175  | 0.0644          | 0.7544    | 0.8090 | 0.7807 | 0.9824   |
| 0.0203        | 7.69  | 200  | 0.0670          | 0.6922    | 0.8399 | 0.7589 | 0.9807   |
| 0.0149        | 8.65  | 225  | 0.0680          | 0.7456    | 0.7969 | 0.7704 | 0.9816   |
| 0.0163        | 9.62  | 250  | 0.0757          | 0.7184    | 0.7728 | 0.7446 | 0.9800   |
| 0.0121        | 10.58 | 275  | 0.0764          | 0.6942    | 0.8675 | 0.7712 | 0.9809   |
| 0.0102        | 11.54 | 300  | 0.0720          | 0.7492    | 0.8176 | 0.7819 | 0.9828   |


### Framework versions

- Transformers 4.27.3
- 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": "SETH_5e-5_0.03_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 

SETH\_5e-5\_0.03\_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: 0.0720
* Precision: 0.7492
* Recall: 0.8176
* F1: 0.7819
* Accuracy: 0.9828


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: 500


### Training results



### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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: 500 ### Training results ### Framework versions


* Transformers 4.27.3
* 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: 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: 500 ### Training results ### Framework versions


* Transformers 4.27.3
* 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: 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: 500### Training results### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2