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# Yepes_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: 0.0896
- Precision: 0.5644
- Recall: 0.4461
- F1: 0.4983
- Accuracy: 0.9814

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.9221        | 0.43  | 25   | 0.2037          | 0.0       | 0.0    | 0.0    | 0.9705   |
| 0.2085        | 0.86  | 50   | 0.1930          | 0.0       | 0.0    | 0.0    | 0.9705   |
| 0.1808        | 1.29  | 75   | 0.1849          | 0.0       | 0.0    | 0.0    | 0.9705   |
| 0.1783        | 1.72  | 100  | 0.1334          | 0.0       | 0.0    | 0.0    | 0.9705   |
| 0.1266        | 2.16  | 125  | 0.1131          | 0.2967    | 0.1856 | 0.2284 | 0.9736   |
| 0.1098        | 2.59  | 150  | 0.1064          | 0.3735    | 0.2874 | 0.3249 | 0.9759   |
| 0.1088        | 3.02  | 175  | 0.0997          | 0.4567    | 0.2844 | 0.3506 | 0.9771   |
| 0.09          | 3.45  | 200  | 0.0917          | 0.4034    | 0.3563 | 0.3784 | 0.9773   |
| 0.0906        | 3.88  | 225  | 0.0865          | 0.4281    | 0.3743 | 0.3994 | 0.9781   |
| 0.0883        | 4.31  | 250  | 0.0850          | 0.4879    | 0.3623 | 0.4158 | 0.9796   |
| 0.0718        | 4.74  | 275  | 0.0864          | 0.5741    | 0.3713 | 0.4509 | 0.9808   |
| 0.0661        | 5.17  | 300  | 0.0860          | 0.5055    | 0.4102 | 0.4529 | 0.9799   |
| 0.0659        | 5.6   | 325  | 0.0765          | 0.5056    | 0.4072 | 0.4511 | 0.9799   |
| 0.0599        | 6.03  | 350  | 0.0796          | 0.4832    | 0.4311 | 0.4557 | 0.9795   |
| 0.0518        | 6.47  | 375  | 0.0959          | 0.6058    | 0.4371 | 0.5078 | 0.9809   |
| 0.0552        | 6.9   | 400  | 0.0804          | 0.4676    | 0.4760 | 0.4718 | 0.9781   |
| 0.0517        | 7.33  | 425  | 0.0917          | 0.6070    | 0.4671 | 0.5279 | 0.9815   |
| 0.0425        | 7.76  | 450  | 0.0859          | 0.5714    | 0.4671 | 0.5140 | 0.9807   |
| 0.0379        | 8.19  | 475  | 0.0896          | 0.5644    | 0.4461 | 0.4983 | 0.9814   |


### 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": "Yepes_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 

Yepes\_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: 0.0896
* Precision: 0.5644
* Recall: 0.4461
* F1: 0.4983
* Accuracy: 0.9814


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