---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: albert-base-v2-finetuned-squad2
  results: []
language:
- en
metrics:
- exact_match
- f1
pipeline_tag: question-answering
---

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

## Model description

ALBERTbase fine-tuned on SQuAD 2.0 : Encoder-based Transformer Language model, pretrained with Parameter Reduction techniques and Sentence Order Prediction<br>
Suitable for Question-Answering tasks, predicts answer spans within the context provided.<br>

**Language model:** albert-base-v2  
**Language:** English  
**Downstream-task:** Question-Answering  
**Training data:** Train-set SQuAD 2.0  
**Evaluation data:** Evaluation-set SQuAD 2.0   
**Hardware Accelerator used**: GPU Tesla T4

## Intended uses & limitations

For Question-Answering - 

```python
!pip install transformers
from transformers import pipeline
model_checkpoint = "IProject-10/albert-base-v2-finetuned-squad2"
question_answerer = pipeline("question-answering", model=model_checkpoint)

context = """
🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration
between them. It's straightforward to train your models with one before loading them for inference with the other.
"""

question = "Which deep learning libraries back 🤗 Transformers?"
question_answerer(question=question, context=context)
```

## Results

Evaluation on SQuAD 2.0 validation dataset:

```
 exact: 78.12684241556472,
 f1: 81.54753481344116,
 total: 11873,
 HasAns_exact: 73.80229419703105,
 HasAns_f1: 80.65348867071317,
 HasAns_total: 5928,
 NoAns_exact: 82.4390243902439,
 NoAns_f1: 82.4390243902439,
 NoAns_total: 5945,
 best_exact: 78.12684241556472,
 best_exact_thresh: 0.9990358352661133,
 best_f1: 81.54753481344157,
 best_f1_thresh: 0.9990358352661133,
 total_time_in_seconds: 248.44505145400035,
 samples_per_second: 47.78923923223437,
 latency_in_seconds: 0.020925212789859374
```
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.92          | 1.0   | 8248  | 0.8960          |
| 0.6593        | 2.0   | 16496 | 0.8548          |
| 0.4314        | 3.0   | 24744 | 0.9900          |

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9900
  
### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3