|
--- |
|
base_model: dccuchile/tulio-chilean-spanish-bert |
|
license: cc-by-4.0 |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: not-ner-v1 |
|
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-v1 |
|
|
|
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: 0.1680 |
|
- Accuracy: 0.9337 |
|
- Precision: 0.9334 |
|
- Recall: 0.9337 |
|
- F1: 0.9333 |
|
|
|
## 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: 20 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 1 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
|
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
| 0.3465 | 0.0799 | 200 | 0.3024 | 0.8719 | 0.8787 | 0.8719 | 0.8737 | |
|
| 0.2925 | 0.1599 | 400 | 0.2530 | 0.9045 | 0.9039 | 0.9045 | 0.9041 | |
|
| 0.2362 | 0.2398 | 600 | 0.2383 | 0.9089 | 0.9084 | 0.9089 | 0.9085 | |
|
| 0.239 | 0.3197 | 800 | 0.2083 | 0.9169 | 0.9163 | 0.9169 | 0.9163 | |
|
| 0.2149 | 0.3997 | 1000 | 0.2640 | 0.9130 | 0.9150 | 0.9130 | 0.9109 | |
|
| 0.2171 | 0.4796 | 1200 | 0.1932 | 0.9211 | 0.9214 | 0.9211 | 0.9212 | |
|
| 0.2056 | 0.5596 | 1400 | 0.1962 | 0.9237 | 0.9243 | 0.9237 | 0.9224 | |
|
| 0.1973 | 0.6395 | 1600 | 0.1906 | 0.9258 | 0.9255 | 0.9258 | 0.9256 | |
|
| 0.1912 | 0.7194 | 1800 | 0.1870 | 0.9277 | 0.9275 | 0.9277 | 0.9270 | |
|
| 0.183 | 0.7994 | 2000 | 0.1727 | 0.9318 | 0.9317 | 0.9318 | 0.9318 | |
|
| 0.1672 | 0.8793 | 2200 | 0.1809 | 0.9320 | 0.9318 | 0.9320 | 0.9313 | |
|
| 0.1643 | 0.9592 | 2400 | 0.1680 | 0.9337 | 0.9334 | 0.9337 | 0.9333 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.42.4 |
|
- Pytorch 2.3.1+cu121 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
|
|
