lauraparra28/Albert-base-v2-finetuned-SQuAD2.0

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