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# Yepes_5e-05_0404_ES6_strict_tok

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: 0.0986
- Precision: 0.7635
- Recall: 0.4641
- F1: 0.5773
- Accuracy: 0.9811

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6203        | 0.43  | 25   | 0.2206          | 0.0       | 0.0    | 0.0    | 0.9663   |
| 0.2394        | 0.86  | 50   | 0.1770          | 0.0       | 0.0    | 0.0    | 0.9663   |
| 0.1771        | 1.29  | 75   | 0.1435          | 0.0       | 0.0    | 0.0    | 0.9663   |
| 0.1761        | 1.72  | 100  | 0.1277          | 0.2656    | 0.2036 | 0.2305 | 0.9722   |
| 0.1386        | 2.16  | 125  | 0.1152          | 0.4471    | 0.2275 | 0.3016 | 0.9742   |
| 0.1227        | 2.59  | 150  | 0.1401          | 0.3871    | 0.3234 | 0.3524 | 0.9623   |
| 0.1188        | 3.02  | 175  | 0.0922          | 0.6331    | 0.3204 | 0.4254 | 0.9778   |
| 0.0897        | 3.45  | 200  | 0.1012          | 0.6416    | 0.3323 | 0.4379 | 0.9773   |
| 0.099         | 3.88  | 225  | 0.0885          | 0.5671    | 0.3922 | 0.4637 | 0.9780   |
| 0.1172        | 4.31  | 250  | 0.0858          | 0.5938    | 0.4551 | 0.5153 | 0.9761   |
| 0.0693        | 4.74  | 275  | 0.0899          | 0.8072    | 0.4012 | 0.536  | 0.9785   |
| 0.0686        | 5.17  | 300  | 0.0986          | 0.7635    | 0.4641 | 0.5773 | 0.9811   |


### 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": "Yepes_5e-05_0404_ES6_strict_tok", "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\_5e-05\_0404\_ES6\_strict\_tok
====================================


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: 0.0986
* Precision: 0.7635
* Recall: 0.4641
* F1: 0.5773
* Accuracy: 0.9811


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