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---
language:
- es
tags:
- generated_from_trainer
datasets:
- jorgeortizfuentes/spanish_nominal_groups_conll2003
model-index:
- name: nominal-groups-recognition-bert-base-spanish-wwm-cased
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. -->
# nominal-groups-recognition-bert-base-spanish-wwm-cased
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co./dccuchile/bert-base-spanish-wwm-cased) on the jorgeortizfuentes/spanish_nominal_groups_conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3568
- Ng Precision: 0.7280
- Ng Recall: 0.7767
- Ng F1: 0.7516
- Ng Number: 3198
- Overall Precision: 0.7280
- Overall Recall: 0.7767
- Overall F1: 0.7516
- Overall Accuracy: 0.8992
## 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: 8
- eval_batch_size: 8
- seed: 13
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Ng Precision | Ng Recall | Ng F1 | Ng Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:---------:|:------:|:---------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.3955 | 1.0 | 228 | 0.2778 | 0.7129 | 0.7492 | 0.7306 | 3198 | 0.7129 | 0.7492 | 0.7306 | 0.8924 |
| 0.2186 | 2.0 | 456 | 0.2763 | 0.7318 | 0.7711 | 0.7509 | 3198 | 0.7318 | 0.7711 | 0.7509 | 0.8990 |
| 0.1586 | 3.0 | 684 | 0.2960 | 0.7274 | 0.7733 | 0.7496 | 3198 | 0.7274 | 0.7733 | 0.7496 | 0.8992 |
| 0.119 | 4.0 | 912 | 0.3330 | 0.7283 | 0.7727 | 0.7498 | 3198 | 0.7283 | 0.7727 | 0.7498 | 0.8982 |
| 0.0943 | 5.0 | 1140 | 0.3568 | 0.7280 | 0.7767 | 0.7516 | 3198 | 0.7280 | 0.7767 | 0.7516 | 0.8992 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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