
<!-- 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. -->

# SETH_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: 0.0866
- Precision: 0.7204
- Recall: 0.8468
- F1: 0.7785
- Accuracy: 0.9819

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3731        | 0.96  | 25   | 0.1863          | 0.0       | 0.0    | 0.0    | 0.9583   |
| 0.1139        | 1.92  | 50   | 0.0862          | 0.4524    | 0.6540 | 0.5348 | 0.9664   |
| 0.0654        | 2.88  | 75   | 0.0737          | 0.5914    | 0.8244 | 0.6887 | 0.9739   |
| 0.051         | 3.85  | 100  | 0.0646          | 0.6340    | 0.8227 | 0.7161 | 0.9789   |
| 0.0444        | 4.81  | 125  | 0.0769          | 0.5938    | 0.8554 | 0.7010 | 0.9732   |
| 0.031         | 5.77  | 150  | 0.0660          | 0.6541    | 0.8692 | 0.7465 | 0.9784   |
| 0.026         | 6.73  | 175  | 0.0641          | 0.7186    | 0.8262 | 0.7686 | 0.9814   |
| 0.0217        | 7.69  | 200  | 0.0682          | 0.6985    | 0.8571 | 0.7697 | 0.9813   |
| 0.0167        | 8.65  | 225  | 0.0678          | 0.7246    | 0.7969 | 0.7590 | 0.9809   |
| 0.0129        | 9.62  | 250  | 0.0727          | 0.7488    | 0.7900 | 0.7688 | 0.9825   |
| 0.0107        | 10.58 | 275  | 0.0778          | 0.7242    | 0.8451 | 0.7800 | 0.9818   |
| 0.0085        | 11.54 | 300  | 0.0784          | 0.7188    | 0.8537 | 0.7805 | 0.9820   |
| 0.0064        | 12.5  | 325  | 0.0866          | 0.7204    | 0.8468 | 0.7785 | 0.9819   |


### 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_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\_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: 0.0866
* Precision: 0.7204
* Recall: 0.8468
* F1: 0.7785
* Accuracy: 0.9819


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