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Kiran2004/
Roberta_QCA_Squad

Name: Roberta_QCA_Squad
Availability: InStock

transformers

roberta

question-answering

generated_from_keras_callback

rajpurkar/squad

deepset/roberta-base-squad2

finetune

cc-by-4.0

endpoints_compatible

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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

python

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