metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - squad
duplicated_from: autoevaluate/extractive-question-answering
model-index:
  - name: autoevaluate/extractive-question-answering-not-evaluated
    results:
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: autoevaluate/squad-sample
          type: autoevaluate/squad-sample
          config: autoevaluate--squad-sample
          split: test
        metrics:
          - type: f1
            value: 76.9929
            name: F1
            verified: true
            verifyToken: >-
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          - type: exact_match
            value: 70
            name: Exact Match
            verified: true
            verifyToken: >-
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          - type: loss
            value: 1.1083998680114746
            name: loss
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzUwZGFjMjlhNzMzZGZjZTU5ZDZiYjg0YThiZTA1MDgzOWUxZTQxYmUzNGE2YWYwYTI2YTM1ZDJiNDdmMTcxZCIsInZlcnNpb24iOjF9.udy8AXJOg0hBuhBahq4XcbShp78SDBJz5phkvi4q8EuHEXuBQ1qIxrQbNDpoV2CG8_MBG9EPPtF5d32WiOn2BA

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
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: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.263	1.0	5533	1.2169

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1