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# SETH_2e-5_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: 0.0759
- Precision: 0.6596
- Recall: 0.8537
- F1: 0.7442
- Accuracy: 0.9785

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5486        | 0.96  | 25   | 0.2095          | 0.0       | 0.0    | 0.0    | 0.9583   |
| 0.1393        | 1.92  | 50   | 0.1119          | 0.6094    | 0.2685 | 0.3728 | 0.9627   |
| 0.0842        | 2.88  | 75   | 0.0841          | 0.5249    | 0.7986 | 0.6334 | 0.9737   |
| 0.0627        | 3.85  | 100  | 0.0737          | 0.5532    | 0.8141 | 0.6588 | 0.9753   |
| 0.0602        | 4.81  | 125  | 0.0683          | 0.6       | 0.8726 | 0.7111 | 0.9756   |
| 0.0448        | 5.77  | 150  | 0.0639          | 0.6717    | 0.8451 | 0.7485 | 0.9803   |
| 0.04          | 6.73  | 175  | 0.0655          | 0.6381    | 0.8709 | 0.7365 | 0.9781   |
| 0.0339        | 7.69  | 200  | 0.0621          | 0.6450    | 0.8726 | 0.7418 | 0.9788   |
| 0.0293        | 8.65  | 225  | 0.0639          | 0.6764    | 0.7952 | 0.7310 | 0.9794   |
| 0.0268        | 9.62  | 250  | 0.0648          | 0.6869    | 0.8571 | 0.7626 | 0.9804   |
| 0.0229        | 10.58 | 275  | 0.0710          | 0.6703    | 0.8571 | 0.7523 | 0.9790   |
| 0.0223        | 11.54 | 300  | 0.0668          | 0.7030    | 0.8107 | 0.7530 | 0.9806   |
| 0.0199        | 12.5  | 325  | 0.0726          | 0.7072    | 0.8313 | 0.7642 | 0.9803   |
| 0.018         | 13.46 | 350  | 0.0759          | 0.6596    | 0.8537 | 0.7442 | 0.9785   |


### 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_2e-5_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\_2e-5\_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: 0.0759
* Precision: 0.6596
* Recall: 0.8537
* F1: 0.7442
* Accuracy: 0.9785


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



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 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 4.27.3
* 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: 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 4.27.3
* 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: 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 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2