Model description
This model is a fine-tuned version of deepset/roberta-base-squad2 on an squad dataset.It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering for 6 Epochs. It achieves the following results after training:
- Train Loss: 0.1434
- Validation Loss: 0.4821
Model Training
- Training Dataset: squad
- Pretrained Model: deepset/roberta-base-squad2
Evaluation
The model's performance can be evaluated using various metrics such as Accuracy, Recall, Precision, F1 score.
- Accuracy: 0.9100
- Precision: 0.9099
- Recall: 0.9099
- F1 Score: 0.9603
Example Usage
from transformers import pipeline
model_name = "Kiran2004/Roberta_QCA_Squad"
question_answerer = pipeline("question-answering", model = model_name)
question = "How many programming languages does BLOOM support?"
context = "BLOOM has 176 billion parameters and can generate text in 46 languages natural languages and 13 programming languages."
question_answerer(question=question, context=context)
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': 2e-05, 'decay_steps': 250, '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 |
---|---|---|
0.5774 | 0.4305 | 0 |
0.3089 | 0.4597 | 1 |
0.2268 | 0.4541 | 2 |
0.1852 | 0.4718 | 3 |
0.1618 | 0.4821 | 4 |
0.1434 | 0.4821 | 5 |
Framework versions
- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
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