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# SETH_2e-05_250

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: 0.0676
- Precision: 0.7820
- Recall: 0.7891
- F1: 0.7855
- Accuracy: 0.9837

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4635        | 0.76  | 25   | 0.1662          | 0.0       | 0.0    | 0.0    | 0.9625   |
| 0.0991        | 1.52  | 50   | 0.0805          | 0.7425    | 0.6291 | 0.6811 | 0.9770   |
| 0.0585        | 2.27  | 75   | 0.0616          | 0.6952    | 0.7836 | 0.7368 | 0.9801   |
| 0.0495        | 3.03  | 100  | 0.0564          | 0.7129    | 0.7945 | 0.7515 | 0.9819   |
| 0.0413        | 3.79  | 125  | 0.0531          | 0.7188    | 0.8273 | 0.7692 | 0.9824   |
| 0.0393        | 4.55  | 150  | 0.0512          | 0.7350    | 0.8218 | 0.7760 | 0.9827   |
| 0.0317        | 5.3   | 175  | 0.0490          | 0.7543    | 0.7927 | 0.7730 | 0.9832   |
| 0.0283        | 6.06  | 200  | 0.0546          | 0.7780    | 0.7836 | 0.7808 | 0.9833   |
| 0.0255        | 6.82  | 225  | 0.0524          | 0.7504    | 0.7818 | 0.7658 | 0.9829   |
| 0.022         | 7.58  | 250  | 0.0567          | 0.7613    | 0.7945 | 0.7776 | 0.9835   |
| 0.0183        | 8.33  | 275  | 0.0566          | 0.7730    | 0.7927 | 0.7828 | 0.9842   |
| 0.0179        | 9.09  | 300  | 0.0592          | 0.7668    | 0.7655 | 0.7662 | 0.9830   |
| 0.016         | 9.85  | 325  | 0.0648          | 0.7855    | 0.7855 | 0.7855 | 0.9841   |
| 0.0135        | 10.61 | 350  | 0.0639          | 0.7732    | 0.7873 | 0.7802 | 0.9832   |
| 0.0121        | 11.36 | 375  | 0.0676          | 0.7820    | 0.7891 | 0.7855 | 0.9837   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "SETH_2e-05_250", "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\_2e-05\_250
================


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: 0.0676
* Precision: 0.7820
* Recall: 0.7891
* F1: 0.7855
* Accuracy: 0.9837


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 2.11.0
* 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 2.11.0
* 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: 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 2.11.0
* 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: 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 2.11.0
* Tokenizers 0.13.2