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# Yepes_0.0001_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: 0.1555
- Precision: 0.5922
- Recall: 0.4552
- F1: 0.5148
- Accuracy: 0.9768

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4065        | 1.39  | 25   | 0.2115          | 0.0       | 0.0    | 0.0    | 0.9672   |
| 0.1995        | 2.78  | 50   | 0.2120          | 0.0       | 0.0    | 0.0    | 0.9672   |
| 0.1995        | 4.17  | 75   | 0.2108          | 0.0       | 0.0    | 0.0    | 0.9672   |
| 0.1694        | 5.56  | 100  | 0.1646          | 0.0       | 0.0    | 0.0    | 0.9672   |
| 0.1493        | 6.94  | 125  | 0.1513          | 0.0       | 0.0    | 0.0    | 0.9672   |
| 0.1266        | 8.33  | 150  | 0.1446          | 0.0       | 0.0    | 0.0    | 0.9672   |
| 0.106         | 9.72  | 175  | 0.1396          | 0.4019    | 0.2139 | 0.2792 | 0.9704   |
| 0.086         | 11.11 | 200  | 0.1162          | 0.5037    | 0.3408 | 0.4065 | 0.9740   |
| 0.0613        | 12.5  | 225  | 0.1230          | 0.5015    | 0.4104 | 0.4514 | 0.9740   |
| 0.047         | 13.89 | 250  | 0.1306          | 0.5333    | 0.4378 | 0.4809 | 0.9753   |
| 0.0351        | 15.28 | 275  | 0.1351          | 0.5629    | 0.4453 | 0.4972 | 0.9757   |
| 0.0266        | 16.67 | 300  | 0.1453          | 0.5617    | 0.4303 | 0.4873 | 0.9765   |
| 0.02          | 18.06 | 325  | 0.1441          | 0.5573    | 0.4478 | 0.4966 | 0.9757   |
| 0.0153        | 19.44 | 350  | 0.1555          | 0.5922    | 0.4552 | 0.5148 | 0.9768   |


### 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": "Yepes_0.0001_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 

Yepes\_0.0001\_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: 0.1555
* Precision: 0.5922
* Recall: 0.4552
* F1: 0.5148
* Accuracy: 0.9768


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: 0.0001
* 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: 0.0001
* 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: 0.0001
* 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: 0.0001
* 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