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

<!-- 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