---
license: apache-2.0
base_model: albert-base-v2
tags:
  - generated_from_trainer
datasets:
  - squad
model-index:
  - name: albert-base-v2-finetuned-squad
    results:
      - task:
          name: Question Answering
          type: question-answering
        dataset:
          type: squad_v2
          name: The Stanford Question Answering Dataset
          args: en
        metrics:
          - type: eval_exact
            value: 76.263
          - type: eval_f1
            value: 84.734
language:
  - en
metrics:
  - exact_match
  - f1

---

<!-- 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. -->

# albert-base-v2-finetuned-squad

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4539
- Exact Match: 80.60548722800378
- F1 score: 88.76870326468953

## Model description

This model is fine-tuned on the extractive question answering task -- The Stanford Question Answering Dataset -- SQuAD2.0.

## Intended uses & limitations

More information needed

## Training and evaluation data

Training and evaluation was done on SQuAD2.0.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-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: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.8702        | 1.0   | 5540  | 0.8943          |
| 0.6972        | 2.0   | 11080 | 0.9087          |
| 0.4998        | 3.0   | 16620 | 0.9890          |
| 0.3601        | 4.0   | 22160 | 1.1892          |
| 0.235         | 5.0   | 27700 | 1.4539          |


### Framework versions

- Transformers 4.34.0
- Pytorch 1.12.1
- Datasets 2.14.5
- Tokenizers 0.14.1