Question Answering
Transformers
TensorFlow
TensorBoard
Spanish
distilbert
generated_from_keras_callback
Instructions to use Sebastian77/distilbert-base-uncased-finetuned-squad_es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastian77/distilbert-base-uncased-finetuned-squad_es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Sebastian77/distilbert-base-uncased-finetuned-squad_es")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Sebastian77/distilbert-base-uncased-finetuned-squad_es") model = AutoModelForQuestionAnswering.from_pretrained("Sebastian77/distilbert-base-uncased-finetuned-squad_es") - Notebooks
- Google Colab
- Kaggle
Sebastian77/distilbert-base-uncased-finetuned-squad_es
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 2.1062
- Train End Logits Accuracy: 0.5048
- Train Start Logits Accuracy: 0.4516
- Validation Loss: 2.1434
- Validation End Logits Accuracy: 0.5010
- Validation Start Logits Accuracy: 0.4433
- Epoch: 1
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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18552, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|---|---|---|---|---|---|---|
| 2.7238 | 0.3737 | 0.3295 | 2.2772 | 0.4800 | 0.4195 | 0 |
| 2.1062 | 0.5048 | 0.4516 | 2.1434 | 0.5010 | 0.4433 | 1 |
Framework versions
- Transformers 4.27.3
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
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