metadata

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

albert-base-v2-finetuned-squad

This model is a fine-tuned version of 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