README.md

---
license: cc-by-4.0
base_model: deepset/roberta-base-squad2
tags:
- generated_from_keras_callback
model-index:
- name: Kiran2004/Roberta_QCA_Squad
  results: []
datasets:
- rajpurkar/squad
metrics:
- accuracy
- precision
- recall
- f1
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

## Model description

This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an [squad](https://huggingface.co/datasets/rajpurkar/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](https://huggingface.co/datasets/rajpurkar/squad)
- **Pretrained Model**: [deepset/roberta-base-squad2](https://huggingface.co/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