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# SETH_2e-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: 0.0579
- Precision: 0.7801
- Recall: 0.8485
- F1: 0.8129
- Accuracy: 0.9863

## 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5347        | 0.96  | 25   | 0.2086          | 0.0       | 0.0    | 0.0    | 0.9606   |
| 0.1372        | 1.92  | 50   | 0.0927          | 0.5702    | 0.7762 | 0.6574 | 0.9742   |
| 0.0648        | 2.88  | 75   | 0.0647          | 0.6081    | 0.8279 | 0.7012 | 0.9774   |
| 0.0494        | 3.85  | 100  | 0.0603          | 0.6395    | 0.8244 | 0.7203 | 0.9788   |
| 0.0448        | 4.81  | 125  | 0.0612          | 0.6106    | 0.8744 | 0.7190 | 0.9780   |
| 0.0334        | 5.77  | 150  | 0.0565          | 0.6564    | 0.8847 | 0.7537 | 0.9801   |
| 0.0293        | 6.73  | 175  | 0.0518          | 0.7465    | 0.8365 | 0.7890 | 0.9832   |
| 0.0224        | 7.69  | 200  | 0.0538          | 0.7010    | 0.8675 | 0.7754 | 0.9829   |
| 0.0186        | 8.65  | 225  | 0.0583          | 0.7508    | 0.8554 | 0.7997 | 0.9842   |
| 0.0174        | 9.62  | 250  | 0.0525          | 0.7604    | 0.8795 | 0.8156 | 0.9846   |
| 0.013         | 10.58 | 275  | 0.0642          | 0.7189    | 0.8847 | 0.7932 | 0.9839   |
| 0.0128        | 11.54 | 300  | 0.0572          | 0.7855    | 0.8571 | 0.8198 | 0.9863   |
| 0.0116        | 12.5  | 325  | 0.0579          | 0.7801    | 0.8485 | 0.8129 | 0.9863   |


### 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_2e-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\_2e-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: 0.0579
* Precision: 0.7801
* Recall: 0.8485
* F1: 0.8129
* Accuracy: 0.9863


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


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