# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Lisibonny/modelo_qa_beto_squad_es_pdqa")
model = AutoModelForQuestionAnswering.from_pretrained("Lisibonny/modelo_qa_beto_squad_es_pdqa", device_map="auto")Quick Links
modelo_qa_beto_squad_es_pdqa
This model is a fine-tuned version of lisibonny/modelo_qa_beto_squad_es on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8463
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 |
|---|---|---|---|
| 1.904 | 1.0 | 4 | 1.1193 |
| 1.1544 | 2.0 | 8 | 0.9157 |
| 0.7543 | 3.0 | 12 | 0.8581 |
| 0.6753 | 4.0 | 16 | 0.8463 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.15.1
Evaluaci贸n oficial del baseline
El modelo fue evaluado sobre la divisi贸n validation del dataset
Lisibonny/pdqa.
| M茅trica | Resultado |
|---|---|
| Exact Match | 60.00 |
| F1 | 75.84 |
| Ejemplos evaluados | 15 |
Configuraci贸n de evaluaci贸n
- Modelo:
Lisibonny/modelo_qa_beto_squad_es_pdqa - Dataset de origen:
Lisibonny/pdqa - Dataset con divisiones oficiales:
Lisibonny/pdqa - Divisi贸n:
validation - Tarea: Question Answering extractivo
- Semilla utilizada para seleccionar validation:
42 - Fecha de evaluaci贸n UTC:
2026-07-20T22:00:44.131074+00:00
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Lisibonny/modelo_qa_beto_squad_es_pdqa")