# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Kiran2004/Electra_QCA_Custom")
model = AutoModelForQuestionAnswering.from_pretrained("Kiran2004/Electra_QCA_Custom", device_map="auto")Quick Links
Kiran2004/Electra_QCA_Custom
This model is a fine-tuned version of deepset/electra-base-squad2 on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0010
- Validation Loss: 0.0001
- Epoch: 3
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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 100, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 1.8202 | 0.0015 | 0 |
| 0.0166 | 0.0002 | 1 |
| 0.0027 | 0.0001 | 2 |
| 0.0010 | 0.0001 | 3 |
Framework versions
- Transformers 4.40.0
- TensorFlow 2.15.0
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for Kiran2004/Electra_QCA_Custom
Base model
deepset/electra-base-squad2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Kiran2004/Electra_QCA_Custom")