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distilbert-finetuned-uncased-squad_v2

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metadata
tags:
  - generated_from_trainer
datasets:
  - squad_v2
model-index:
  - name: distilbert-finetuned-uncased-squad_v2
    results:
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: SQuAD v2
          type: squad_v2
          split: validation
        metrics:
          - type: exact
            value: 100
            name: Exact
          - type: f1
            value: 100
            name: F1
          - type: total
            value: 2
            name: Total
          - type: HasAns_exact
            value: 100
            name: Hasans_exact
          - type: HasAns_f1
            value: 100
            name: Hasans_f1
          - type: HasAns_total
            value: 2
            name: Hasans_total
          - type: best_exact
            value: 100
            name: Best_exact
          - type: best_exact_thresh
            value: 0.967875599861145
            name: Best_exact_thresh
          - type: best_f1
            value: 100
            name: Best_f1
          - type: best_f1_thresh
            value: 0.967875599861145
            name: Best_f1_thresh
          - type: total_time_in_seconds
            value: 0.03511825300000737
            name: Total_time_in_seconds
          - type: samples_per_second
            value: 56.9504411281387
            name: Samples_per_second
          - type: latency_in_seconds
            value: 0.017559126500003686
            name: Latency_in_seconds

distilbert-finetuned-uncased-squad_v2

This model was trained from scratch on the squad_v2 dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2617

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: 64
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss
3.6437 0.39 100 2.1780
2.1596 0.78 200 1.6557
1.8138 1.18 300 1.5683
1.6987 1.57 400 1.5076
1.6586 1.96 500 1.5350
1.5957 1.18 600 1.4431
1.5825 1.37 700 1.4955
1.5523 1.57 800 1.4444
1.5346 1.76 900 1.3930
1.5098 1.96 1000 1.4285
1.4632 2.16 1100 1.3630
1.4468 2.35 1200 1.3710
1.4343 2.55 1300 1.3422
1.4225 2.75 1400 1.3971
1.408 2.94 1500 1.4355
1.3609 3.14 1600 1.3332
1.3398 3.33 1700 1.3792
1.3224 3.53 1800 1.4172
1.3152 3.73 1900 1.3956
1.3141 3.92 2000 1.3748
1.3085 2.06 2100 1.3949
1.3325 2.16 2200 1.4870
1.3162 2.26 2300 1.4565
1.2936 2.35 2400 1.4496
1.2648 2.45 2500 1.2868
1.2531 2.55 2600 1.5094
1.2599 2.65 2700 1.3451
1.2545 2.75 2800 1.4071
1.2461 2.84 2900 1.3378
1.2038 2.94 3000 1.2946
1.1677 3.04 3100 1.4802
1.103 3.14 3200 1.3580
1.1205 3.24 3300 1.3819
1.095 3.33 3400 1.4336
1.0896 3.43 3500 1.4963
1.0856 3.53 3600 1.3384
1.0652 3.63 3700 1.3583
1.0859 3.73 3800 1.4140
1.058 3.83 3900 1.2617
1.0724 3.92 4000 1.3552
1.0509 4.02 4100 1.2971
0.97 4.12 4200 1.3268
0.95 4.22 4300 1.3754
0.9337 4.32 4400 1.3687
0.977 4.41 4500 1.3613
0.9484 4.51 4600 1.5139
0.9739 4.61 4700 1.2861
0.955 4.71 4800 1.3667
0.9536 4.81 4900 1.3180
0.9541 4.9 5000 1.4611
0.9462 5.0 5100 1.4067
0.8728 5.1 5200 1.3490
0.8646 5.2 5300 1.4631
0.8683 5.3 5400 1.4978
0.8571 5.39 5500 1.5814
0.8475 5.49 5600 1.5535
0.8653 5.59 5700 1.4938
0.8664 5.69 5800 1.4141
0.889 5.79 5900 1.4487
0.8601 5.88 6000 1.4722
0.8645 5.98 6100 1.5843
0.785 6.08 6200 1.6028
0.7711 6.18 6300 1.6271
0.8056 6.28 6400 1.5399
0.8087 6.37 6500 1.4927
0.7859 6.47 6600 1.4677
0.7896 6.57 6700 1.4780
0.7971 6.67 6800 1.5110
0.7952 6.77 6900 1.5459
0.7971 6.87 7000 1.5282
0.7908 6.96 7100 1.4799
0.7456 7.06 7200 1.6487
0.7236 7.16 7300 1.6543
0.7484 7.26 7400 1.6202

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1