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# Yepes_2e-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: 0.1279
- Precision: 0.6833
- Recall: 0.5100
- F1: 0.5840
- Accuracy: 0.9788

## 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: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.8467        | 1.39  | 25   | 0.2149          | 0.0       | 0.0    | 0.0    | 0.9672   |
| 0.1988        | 2.78  | 50   | 0.1959          | 0.0       | 0.0    | 0.0    | 0.9672   |
| 0.156         | 4.17  | 75   | 0.1439          | 0.3268    | 0.2065 | 0.2530 | 0.9691   |
| 0.1128        | 5.56  | 100  | 0.1324          | 0.49      | 0.2438 | 0.3256 | 0.9730   |
| 0.0978        | 6.94  | 125  | 0.1222          | 0.4964    | 0.3433 | 0.4059 | 0.9747   |
| 0.0788        | 8.33  | 150  | 0.1154          | 0.5193    | 0.3682 | 0.4309 | 0.9760   |
| 0.067         | 9.72  | 175  | 0.1162          | 0.4711    | 0.3856 | 0.4241 | 0.9749   |
| 0.058         | 11.11 | 200  | 0.1236          | 0.5275    | 0.3582 | 0.4267 | 0.9761   |
| 0.0491        | 12.5  | 225  | 0.1177          | 0.4940    | 0.4104 | 0.4484 | 0.9754   |
| 0.0443        | 13.89 | 250  | 0.1235          | 0.5472    | 0.4179 | 0.4739 | 0.9767   |
| 0.0383        | 15.28 | 275  | 0.1198          | 0.5764    | 0.4502 | 0.5056 | 0.9770   |
| 0.0369        | 16.67 | 300  | 0.1219          | 0.5892    | 0.4602 | 0.5168 | 0.9776   |
| 0.0326        | 18.06 | 325  | 0.1261          | 0.7       | 0.4701 | 0.5625 | 0.9790   |
| 0.0305        | 19.44 | 350  | 0.1269          | 0.6904    | 0.4826 | 0.5681 | 0.9787   |
| 0.0269        | 20.83 | 375  | 0.1252          | 0.6656    | 0.5    | 0.5710 | 0.9783   |
| 0.025         | 22.22 | 400  | 0.1253          | 0.6529    | 0.5100 | 0.5726 | 0.9782   |
| 0.0244        | 23.61 | 425  | 0.1284          | 0.6875    | 0.4925 | 0.5739 | 0.9790   |
| 0.0224        | 25.0  | 450  | 0.1279          | 0.6833    | 0.5100 | 0.5840 | 0.9788   |


### 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_2e-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 

Yepes\_2e-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: 0.1279
* Precision: 0.6833
* Recall: 0.5100
* F1: 0.5840
* Accuracy: 0.9788


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: 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: 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: 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: 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: 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: 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: 500### Training results### Framework versions


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