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# SETH_5e-05_0404_ES6_strict

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: 0.0633
- Precision: 0.7953
- Recall: 0.8692
- F1: 0.8306
- Accuracy: 0.9864

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3171        | 0.96  | 25   | 0.0921          | 0.6399    | 0.7676 | 0.6980 | 0.9759   |
| 0.0656        | 1.92  | 50   | 0.0588          | 0.7528    | 0.8227 | 0.7862 | 0.9796   |
| 0.04          | 2.88  | 75   | 0.0456          | 0.7641    | 0.8640 | 0.8110 | 0.9837   |
| 0.031         | 3.85  | 100  | 0.0481          | 0.7647    | 0.8726 | 0.8151 | 0.9840   |
| 0.0241        | 4.81  | 125  | 0.0443          | 0.7915    | 0.8623 | 0.8254 | 0.9857   |
| 0.0162        | 5.77  | 150  | 0.0469          | 0.8443    | 0.8399 | 0.8421 | 0.9868   |
| 0.0132        | 6.73  | 175  | 0.0487          | 0.8310    | 0.8296 | 0.8303 | 0.9865   |
| 0.013         | 7.69  | 200  | 0.0545          | 0.7692    | 0.8778 | 0.8199 | 0.9854   |
| 0.0091        | 8.65  | 225  | 0.0539          | 0.8093    | 0.8399 | 0.8243 | 0.9865   |
| 0.0071        | 9.62  | 250  | 0.0691          | 0.7820    | 0.8520 | 0.8155 | 0.9855   |
| 0.0049        | 10.58 | 275  | 0.0633          | 0.7953    | 0.8692 | 0.8306 | 0.9864   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "SETH_5e-05_0404_ES6_strict", "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\_strict
==============================


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: 0.0633
* Precision: 0.7953
* Recall: 0.8692
* F1: 0.8306
* Accuracy: 0.9864


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 0.13.3



### 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 0.13.3
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 0.13.3
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 0.13.3