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---
license: cc-by-4.0
base_model: dccuchile/tulio-chilean-spanish-bert
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: not-ner-v2_16batch
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# not-ner-v2_16batch

This model is a fine-tuned version of [dccuchile/tulio-chilean-spanish-bert](https://huggingface.co/dccuchile/tulio-chilean-spanish-bert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1124
- Accuracy: 0.9662
- Precision: 0.9671
- Recall: 0.9662
- F1: 0.9665

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0906        | 0.0798 | 250  | 0.1124          | 0.9662   | 0.9671    | 0.9662 | 0.9665 |
| 0.1008        | 0.1596 | 500  | 0.1416          | 0.9654   | 0.9652    | 0.9654 | 0.9653 |
| 0.0918        | 0.2394 | 750  | 0.1361          | 0.9662   | 0.9657    | 0.9662 | 0.9659 |
| 0.1077        | 0.3192 | 1000 | 0.1353          | 0.9636   | 0.9626    | 0.9636 | 0.9627 |
| 0.0863        | 0.3990 | 1250 | 0.1457          | 0.9590   | 0.9624    | 0.9590 | 0.9601 |
| 0.1048        | 0.4788 | 1500 | 0.1340          | 0.9653   | 0.9645    | 0.9653 | 0.9648 |
| 0.1066        | 0.5586 | 1750 | 0.1490          | 0.9631   | 0.9621    | 0.9631 | 0.9624 |
| 0.107         | 0.6384 | 2000 | 0.1479          | 0.9638   | 0.9641    | 0.9638 | 0.9639 |
| 0.1274        | 0.7182 | 2250 | 0.1331          | 0.9636   | 0.9634    | 0.9636 | 0.9635 |
| 0.1381        | 0.7980 | 2500 | 0.1577          | 0.9626   | 0.9616    | 0.9626 | 0.9618 |
| 0.1111        | 0.8778 | 2750 | 0.1353          | 0.9625   | 0.9637    | 0.9625 | 0.9630 |
| 0.132         | 0.9575 | 3000 | 0.1785          | 0.9592   | 0.9578    | 0.9592 | 0.9579 |
| 0.1464        | 1.0373 | 3250 | 0.1712          | 0.9610   | 0.9598    | 0.9610 | 0.9600 |
| 0.1279        | 1.1171 | 3500 | 0.1514          | 0.9630   | 0.9622    | 0.9630 | 0.9625 |
| 0.0947        | 1.1969 | 3750 | 0.1554          | 0.9625   | 0.9629    | 0.9625 | 0.9627 |
| 0.0958        | 1.2767 | 4000 | 0.1746          | 0.9617   | 0.9606    | 0.9617 | 0.9607 |
| 0.1212        | 1.3565 | 4250 | 0.1494          | 0.9628   | 0.9620    | 0.9628 | 0.9622 |
| 0.1097        | 1.4363 | 4500 | 0.1557          | 0.9635   | 0.9627    | 0.9635 | 0.9630 |
| 0.1288        | 1.5161 | 4750 | 0.1499          | 0.9642   | 0.9633    | 0.9642 | 0.9635 |
| 0.1043        | 1.5959 | 5000 | 0.1488          | 0.9614   | 0.9619    | 0.9614 | 0.9617 |
| 0.1016        | 1.6757 | 5250 | 0.1521          | 0.9616   | 0.9611    | 0.9616 | 0.9613 |
| 0.1073        | 1.7555 | 5500 | 0.1455          | 0.9618   | 0.9621    | 0.9618 | 0.9619 |
| 0.1264        | 1.8353 | 5750 | 0.2369          | 0.9143   | 0.9407    | 0.9143 | 0.9218 |
| 0.1211        | 1.9151 | 6000 | 0.1524          | 0.9613   | 0.9606    | 0.9613 | 0.9609 |
| 0.1071        | 1.9949 | 6250 | 0.1327          | 0.9574   | 0.9565    | 0.9574 | 0.9548 |
| 0.0915        | 2.0747 | 6500 | 0.1325          | 0.9640   | 0.9638    | 0.9640 | 0.9639 |
| 0.084         | 2.1545 | 6750 | 0.1321          | 0.9647   | 0.9646    | 0.9647 | 0.9646 |
| 0.0922        | 2.2343 | 7000 | 0.1486          | 0.9644   | 0.9635    | 0.9644 | 0.9637 |
| 0.0877        | 2.3141 | 7250 | 0.1281          | 0.9643   | 0.9648    | 0.9643 | 0.9645 |
| 0.0828        | 2.3939 | 7500 | 0.1425          | 0.9575   | 0.9591    | 0.9575 | 0.9582 |
| 0.0859        | 2.4737 | 7750 | 0.1283          | 0.9670   | 0.9665    | 0.9670 | 0.9667 |
| 0.0866        | 2.5535 | 8000 | 0.1189          | 0.9677   | 0.9671    | 0.9677 | 0.9673 |
| 0.0681        | 2.6333 | 8250 | 0.1195          | 0.9689   | 0.9682    | 0.9689 | 0.9683 |
| 0.0462        | 2.7131 | 8500 | 0.1408          | 0.9678   | 0.9670    | 0.9678 | 0.9670 |
| 0.0672        | 2.7929 | 8750 | 0.1233          | 0.9690   | 0.9684    | 0.9690 | 0.9686 |
| 0.0578        | 2.8726 | 9000 | 0.1232          | 0.9694   | 0.9689    | 0.9694 | 0.9691 |
| 0.0591        | 2.9524 | 9250 | 0.1219          | 0.9694   | 0.9689    | 0.9694 | 0.9690 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1