autoevaluate/extractive-question-answering

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extractive-question-answering

This model is a fine-tuned version of distilbert-base-uncased on the squad dataset. It achieves the following results on the evaluation set:

{'exact_match': 72.95175023651845,
 'f1': 81.85552166092225,
 'latency_in_seconds': 0.008616470915042614,
 'samples_per_second': 116.05679516125359,
 'total_time_in_seconds': 91.07609757200044}

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 2e-05
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 1

Training results

Framework versions

• —Transformers 4.19.2
• —Pytorch 1.11.0+cu113
• —Datasets 2.2.2
• —Tokenizers 0.12.1