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# SETH_5e-05_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: 0.0650
- Precision: 0.7754
- Recall: 0.8675
- F1: 0.8188
- Accuracy: 0.9857

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.331         | 0.96  | 25   | 0.1111          | 0.3370    | 0.6265 | 0.4383 | 0.9582   |
| 0.0683        | 1.92  | 50   | 0.0626          | 0.7098    | 0.8210 | 0.7614 | 0.9796   |
| 0.0423        | 2.88  | 75   | 0.0547          | 0.7559    | 0.8313 | 0.7918 | 0.9827   |
| 0.0342        | 3.85  | 100  | 0.0527          | 0.6795    | 0.8795 | 0.7667 | 0.9805   |
| 0.0298        | 4.81  | 125  | 0.0574          | 0.6802    | 0.8933 | 0.7723 | 0.9804   |
| 0.02          | 5.77  | 150  | 0.0476          | 0.7457    | 0.8124 | 0.7776 | 0.9837   |
| 0.0165        | 6.73  | 175  | 0.0520          | 0.7845    | 0.8210 | 0.8024 | 0.9852   |
| 0.0145        | 7.69  | 200  | 0.0645          | 0.7075    | 0.8950 | 0.7903 | 0.9828   |
| 0.0092        | 8.65  | 225  | 0.0620          | 0.7945    | 0.8451 | 0.8190 | 0.9863   |
| 0.0083        | 9.62  | 250  | 0.0727          | 0.7426    | 0.8692 | 0.8010 | 0.9836   |
| 0.0054        | 10.58 | 275  | 0.0628          | 0.8       | 0.8330 | 0.8162 | 0.9861   |
| 0.0058        | 11.54 | 300  | 0.0650          | 0.7754    | 0.8675 | 0.8188 | 0.9857   |


### 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": "SETH_5e-05_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 

SETH\_5e-05\_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: 0.0650
* Precision: 0.7754
* Recall: 0.8675
* F1: 0.8188
* Accuracy: 0.9857


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 0.13.2



### 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: 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: 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 0.13.2
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: 2000### Training results### Framework versions


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