Instructions to use Lisibonny/modelo_qa_beto_squad_es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lisibonny/modelo_qa_beto_squad_es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Lisibonny/modelo_qa_beto_squad_es")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Lisibonny/modelo_qa_beto_squad_es") model = AutoModelForQuestionAnswering.from_pretrained("Lisibonny/modelo_qa_beto_squad_es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
modelo_qa_beto_squad_es
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.9490
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.7331 | 1.0 | 5427 | 1.7603 |
| 1.4284 | 2.0 | 10854 | 1.7249 |
| 1.1215 | 3.0 | 16281 | 1.7807 |
| 0.9221 | 4.0 | 21708 | 1.9490 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.15.1
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Model tree for Lisibonny/modelo_qa_beto_squad_es
Base model
dccuchile/bert-base-spanish-wwm-uncased