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# SETH_5e-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: 0.0716
- Precision: 0.7964
- Recall: 0.8036
- F1: 0.8000
- Accuracy: 0.9849

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3757        | 0.76  | 25   | 0.1924          | 0.0       | 0.0    | 0.0    | 0.9625   |
| 0.1119        | 1.52  | 50   | 0.0723          | 0.6237    | 0.7473 | 0.6799 | 0.9775   |
| 0.0565        | 2.27  | 75   | 0.0614          | 0.6569    | 0.7727 | 0.7101 | 0.9794   |
| 0.048         | 3.03  | 100  | 0.0586          | 0.6667    | 0.8655 | 0.7532 | 0.9801   |
| 0.0355        | 3.79  | 125  | 0.0519          | 0.7206    | 0.8345 | 0.7734 | 0.9835   |
| 0.0328        | 4.55  | 150  | 0.0532          | 0.7165    | 0.8455 | 0.7756 | 0.9831   |
| 0.0258        | 5.3   | 175  | 0.0539          | 0.7460    | 0.8382 | 0.7894 | 0.9835   |
| 0.022         | 6.06  | 200  | 0.0561          | 0.7612    | 0.7709 | 0.7660 | 0.9836   |
| 0.0189        | 6.82  | 225  | 0.0564          | 0.7636    | 0.74   | 0.7516 | 0.9828   |
| 0.0166        | 7.58  | 250  | 0.0597          | 0.7274    | 0.8491 | 0.7836 | 0.9836   |
| 0.0128        | 8.33  | 275  | 0.0626          | 0.8251    | 0.7636 | 0.7932 | 0.9854   |
| 0.0113        | 9.09  | 300  | 0.0603          | 0.8029    | 0.8    | 0.8015 | 0.9854   |
| 0.009         | 9.85  | 325  | 0.0687          | 0.8026    | 0.7909 | 0.7967 | 0.9857   |
| 0.0075        | 10.61 | 350  | 0.0716          | 0.7964    | 0.8036 | 0.8000 | 0.9849   |


### 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_5e-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\_5e-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: 0.0716
* Precision: 0.7964
* Recall: 0.8036
* F1: 0.8000
* Accuracy: 0.9849


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 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 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 4.27.4
* 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: 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 4.27.4
* 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: 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 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2