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# custom-ner-model

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: 0.2706
- Precision: 0.8139
- Recall: 0.8624
- F1: 0.8374
- Accuracy: 0.9376

## 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   | 55   | 0.2457          | 0.7716    | 0.8211 | 0.7956 | 0.9346   |
| No log        | 2.0   | 110  | 0.3008          | 0.75      | 0.8257 | 0.7860 | 0.9152   |
| No log        | 3.0   | 165  | 0.2658          | 0.7712    | 0.8349 | 0.8018 | 0.9322   |
| No log        | 4.0   | 220  | 0.2580          | 0.7583    | 0.8349 | 0.7948 | 0.9322   |
| No log        | 5.0   | 275  | 0.2610          | 0.8079    | 0.8486 | 0.8277 | 0.9346   |
| No log        | 6.0   | 330  | 0.2660          | 0.7699    | 0.8440 | 0.8053 | 0.9303   |
| No log        | 7.0   | 385  | 0.2910          | 0.7830    | 0.8440 | 0.8124 | 0.9261   |
| No log        | 8.0   | 440  | 0.2577          | 0.7913    | 0.8349 | 0.8125 | 0.9376   |
| No log        | 9.0   | 495  | 0.2629          | 0.7948    | 0.8349 | 0.8143 | 0.9346   |
| 0.0437        | 10.0  | 550  | 0.2745          | 0.8070    | 0.8440 | 0.8251 | 0.9328   |
| 0.0437        | 11.0  | 605  | 0.2733          | 0.7860    | 0.8257 | 0.8054 | 0.9316   |
| 0.0437        | 12.0  | 660  | 0.2641          | 0.7965    | 0.8440 | 0.8196 | 0.9310   |
| 0.0437        | 13.0  | 715  | 0.2716          | 0.8139    | 0.8624 | 0.8374 | 0.9334   |
| 0.0437        | 14.0  | 770  | 0.2765          | 0.8210    | 0.8624 | 0.8412 | 0.9334   |
| 0.0437        | 15.0  | 825  | 0.2775          | 0.8253    | 0.8670 | 0.8456 | 0.9346   |
| 0.0437        | 16.0  | 880  | 0.2737          | 0.7897    | 0.8440 | 0.8160 | 0.9340   |
| 0.0437        | 17.0  | 935  | 0.2691          | 0.8043    | 0.8486 | 0.8259 | 0.9388   |
| 0.0437        | 18.0  | 990  | 0.2694          | 0.8246    | 0.8624 | 0.8430 | 0.9382   |
| 0.0157        | 19.0  | 1045 | 0.2732          | 0.8069    | 0.8624 | 0.8337 | 0.9346   |
| 0.0157        | 20.0  | 1100 | 0.2706          | 0.8139    | 0.8624 | 0.8374 | 0.9376   |


### 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"], "model-index": [{"name": "custom-ner-model", "results": []}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

custom-ner-model
================


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: 0.2706
* Precision: 0.8139
* Recall: 0.8624
* F1: 0.8374
* Accuracy: 0.9376


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 #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 #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