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# custom-ner-model2

This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2050
- Precision: 0.8542
- Recall: 0.8817
- F1: 0.8677
- Accuracy: 0.9595

## 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
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 105  | 0.5185          | 0.5840    | 0.5484 | 0.5656 | 0.8596   |
| No log        | 2.0   | 210  | 0.3212          | 0.7365    | 0.7312 | 0.7338 | 0.9050   |
| No log        | 3.0   | 315  | 0.2440          | 0.8123    | 0.8065 | 0.8094 | 0.9389   |
| No log        | 4.0   | 420  | 0.2186          | 0.8014    | 0.8100 | 0.8057 | 0.9431   |
| 0.4107        | 5.0   | 525  | 0.1911          | 0.8481    | 0.8602 | 0.8541 | 0.9516   |
| 0.4107        | 6.0   | 630  | 0.1931          | 0.8235    | 0.8530 | 0.8380 | 0.9546   |
| 0.4107        | 7.0   | 735  | 0.1720          | 0.8368    | 0.8638 | 0.8501 | 0.9570   |
| 0.4107        | 8.0   | 840  | 0.1858          | 0.8385    | 0.8746 | 0.8561 | 0.9583   |
| 0.4107        | 9.0   | 945  | 0.1858          | 0.85      | 0.8530 | 0.8515 | 0.9552   |
| 0.0667        | 10.0  | 1050 | 0.1961          | 0.8526    | 0.8710 | 0.8617 | 0.9564   |
| 0.0667        | 11.0  | 1155 | 0.1970          | 0.8537    | 0.8781 | 0.8657 | 0.9589   |
| 0.0667        | 12.0  | 1260 | 0.1865          | 0.8478    | 0.8781 | 0.8627 | 0.9619   |
| 0.0667        | 13.0  | 1365 | 0.1994          | 0.8379    | 0.8710 | 0.8541 | 0.9583   |
| 0.0667        | 14.0  | 1470 | 0.1913          | 0.8507    | 0.8781 | 0.8642 | 0.9613   |
| 0.0274        | 15.0  | 1575 | 0.2064          | 0.8512    | 0.8817 | 0.8662 | 0.9595   |
| 0.0274        | 16.0  | 1680 | 0.2053          | 0.8478    | 0.8781 | 0.8627 | 0.9601   |
| 0.0274        | 17.0  | 1785 | 0.2037          | 0.8576    | 0.8853 | 0.8713 | 0.9601   |
| 0.0274        | 18.0  | 1890 | 0.2056          | 0.8632    | 0.8817 | 0.8723 | 0.9595   |
| 0.0274        | 19.0  | 1995 | 0.2066          | 0.8571    | 0.8817 | 0.8693 | 0.9601   |
| 0.0162        | 20.0  | 2100 | 0.2050          | 0.8542    | 0.8817 | 0.8677 | 0.9595   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "dccuchile/distilbert-base-spanish-uncased", "model-index": [{"name": "custom-ner-model2", "results": []}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer base_model:dccuchile/distilbert-base-spanish-uncased autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #base_model-dccuchile/distilbert-base-spanish-uncased #autotrain_compatible #endpoints_compatible #region-us 

custom-ner-model2
=================


This model is a fine-tuned version of dccuchile/distilbert-base-spanish-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2050
* Precision: 0.8542
* Recall: 0.8817
* F1: 0.8677
* Accuracy: 0.9595


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
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* 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
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #base_model-dccuchile/distilbert-base-spanish-uncased #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
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #base_model-dccuchile/distilbert-base-spanish-uncased #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
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2