Instructions to use NiGuLa/dccuchile_bert-base-spanish-wwm-cased_ep10_lr1e-05_batchpergpu16_gpu1_weighted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NiGuLa/dccuchile_bert-base-spanish-wwm-cased_ep10_lr1e-05_batchpergpu16_gpu1_weighted with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NiGuLa/dccuchile_bert-base-spanish-wwm-cased_ep10_lr1e-05_batchpergpu16_gpu1_weighted")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NiGuLa/dccuchile_bert-base-spanish-wwm-cased_ep10_lr1e-05_batchpergpu16_gpu1_weighted") model = AutoModelForSequenceClassification.from_pretrained("NiGuLa/dccuchile_bert-base-spanish-wwm-cased_ep10_lr1e-05_batchpergpu16_gpu1_weighted", device_map="auto") - Notebooks
- Google Colab
- Kaggle
dccuchile_bert-base-spanish-wwm-cased_ep10_lr1e-05_batchpergpu16_gpu1_weighted
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2989
- F1 Micro: 0.6953
- F1 Macro: 0.3893
- Exact Match: 0.6723
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Exact Match |
|---|---|---|---|---|---|---|
| 0.3646 | 0.9924 | 131 | 0.4025 | 0.6082 | 0.1974 | 0.6307 |
| 0.3934 | 1.9848 | 262 | 0.3667 | 0.6261 | 0.2793 | 0.6420 |
| 0.3214 | 2.9773 | 393 | 0.3171 | 0.6577 | 0.3390 | 0.6572 |
| 0.2660 | 3.9697 | 524 | 0.2899 | 0.6656 | 0.3862 | 0.6686 |
| 0.1906 | 4.9621 | 655 | 0.2866 | 0.6783 | 0.3740 | 0.6705 |
| 0.1556 | 5.9545 | 786 | 0.2913 | 0.6907 | 0.3917 | 0.6345 |
| 0.1517 | 6.9470 | 917 | 0.2880 | 0.7009 | 0.4060 | 0.6913 |
| 0.1313 | 7.9394 | 1048 | 0.3007 | 0.6867 | 0.3850 | 0.6780 |
| 0.1124 | 8.9318 | 1179 | 0.2989 | 0.6953 | 0.3893 | 0.6723 |
Framework versions
- Transformers 5.6.2
- Pytorch 2.11.0+cu130
- Datasets 4.8.4
- Tokenizers 0.22.2
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Model tree for NiGuLa/dccuchile_bert-base-spanish-wwm-cased_ep10_lr1e-05_batchpergpu16_gpu1_weighted
Base model
dccuchile/bert-base-spanish-wwm-cased