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# Yepes_2e-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: 0.0967
- Precision: 0.6635
- Recall: 0.4192
- F1: 0.5138
- Accuracy: 0.9782

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.9417        | 0.43  | 25   | 0.2323          | 0.0       | 0.0    | 0.0    | 0.9663   |
| 0.267         | 0.86  | 50   | 0.2162          | 0.0       | 0.0    | 0.0    | 0.9663   |
| 0.2155        | 1.29  | 75   | 0.1810          | 0.0       | 0.0    | 0.0    | 0.9663   |
| 0.2125        | 1.72  | 100  | 0.1474          | 0.0       | 0.0    | 0.0    | 0.9682   |
| 0.1642        | 2.16  | 125  | 0.1409          | 0.3333    | 0.0958 | 0.1488 | 0.9705   |
| 0.152         | 2.59  | 150  | 0.1251          | 0.3284    | 0.2006 | 0.2491 | 0.9729   |
| 0.1489        | 3.02  | 175  | 0.1130          | 0.3838    | 0.2275 | 0.2857 | 0.9741   |
| 0.1285        | 3.45  | 200  | 0.1114          | 0.6       | 0.2515 | 0.3544 | 0.9749   |
| 0.1214        | 3.88  | 225  | 0.1018          | 0.4064    | 0.3054 | 0.3487 | 0.9754   |
| 0.1576        | 4.31  | 250  | 0.0988          | 0.4760    | 0.3263 | 0.3872 | 0.9749   |
| 0.0958        | 4.74  | 275  | 0.0967          | 0.6270    | 0.3473 | 0.4470 | 0.9774   |
| 0.0952        | 5.17  | 300  | 0.0981          | 0.6686    | 0.3503 | 0.4597 | 0.9784   |
| 0.0852        | 5.6   | 325  | 0.0922          | 0.6649    | 0.3743 | 0.4789 | 0.9782   |
| 0.0817        | 6.03  | 350  | 0.0936          | 0.5865    | 0.4162 | 0.4869 | 0.9772   |
| 0.0755        | 6.47  | 375  | 0.0967          | 0.6635    | 0.4192 | 0.5138 | 0.9782   |


### 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_2e-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\_2e-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: 0.0967
* Precision: 0.6635
* Recall: 0.4192
* F1: 0.5138
* Accuracy: 0.9782


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 0.13.3



### 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 0.13.3
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: 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 0.13.3
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: 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 0.13.3