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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- squad_v2 |
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model-index: |
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- name: distilbert-finetuned-uncased-squad_v2 |
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results: |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: SQuAD v2 |
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type: squad_v2 |
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split: validation |
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metrics: |
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- type: exact |
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value: 100.0 |
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name: Exact |
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- type: f1 |
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value: 100.0 |
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name: F1 |
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- type: total |
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value: 2 |
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name: Total |
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- type: HasAns_exact |
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value: 100.0 |
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name: Hasans_exact |
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- type: HasAns_f1 |
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value: 100.0 |
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name: Hasans_f1 |
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- type: HasAns_total |
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value: 2 |
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name: Hasans_total |
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- type: best_exact |
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value: 100.0 |
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name: Best_exact |
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- type: best_exact_thresh |
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value: 0.967875599861145 |
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name: Best_exact_thresh |
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- type: best_f1 |
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value: 100.0 |
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name: Best_f1 |
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- type: best_f1_thresh |
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value: 0.967875599861145 |
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name: Best_f1_thresh |
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- type: total_time_in_seconds |
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value: 0.03511825300000737 |
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name: Total_time_in_seconds |
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- type: samples_per_second |
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value: 56.9504411281387 |
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name: Samples_per_second |
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- type: latency_in_seconds |
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value: 0.017559126500003686 |
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name: Latency_in_seconds |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilbert-finetuned-uncased-squad_v2 |
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This model was trained from scratch on the squad_v2 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2617 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 3.6437 | 0.39 | 100 | 2.1780 | |
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| 2.1596 | 0.78 | 200 | 1.6557 | |
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| 1.8138 | 1.18 | 300 | 1.5683 | |
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| 1.6987 | 1.57 | 400 | 1.5076 | |
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| 1.6586 | 1.96 | 500 | 1.5350 | |
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| 1.5957 | 1.18 | 600 | 1.4431 | |
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| 1.5825 | 1.37 | 700 | 1.4955 | |
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| 1.5523 | 1.57 | 800 | 1.4444 | |
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| 1.5346 | 1.76 | 900 | 1.3930 | |
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| 1.5098 | 1.96 | 1000 | 1.4285 | |
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| 1.4632 | 2.16 | 1100 | 1.3630 | |
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| 1.4468 | 2.35 | 1200 | 1.3710 | |
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| 1.4343 | 2.55 | 1300 | 1.3422 | |
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| 1.4225 | 2.75 | 1400 | 1.3971 | |
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| 1.408 | 2.94 | 1500 | 1.4355 | |
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| 1.3609 | 3.14 | 1600 | 1.3332 | |
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| 1.3398 | 3.33 | 1700 | 1.3792 | |
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| 1.3224 | 3.53 | 1800 | 1.4172 | |
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| 1.3152 | 3.73 | 1900 | 1.3956 | |
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| 1.3141 | 3.92 | 2000 | 1.3748 | |
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| 1.3085 | 2.06 | 2100 | 1.3949 | |
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| 1.3325 | 2.16 | 2200 | 1.4870 | |
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| 1.3162 | 2.26 | 2300 | 1.4565 | |
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| 1.2936 | 2.35 | 2400 | 1.4496 | |
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| 1.2648 | 2.45 | 2500 | 1.2868 | |
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| 1.2531 | 2.55 | 2600 | 1.5094 | |
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| 1.2599 | 2.65 | 2700 | 1.3451 | |
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| 1.2545 | 2.75 | 2800 | 1.4071 | |
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| 1.2461 | 2.84 | 2900 | 1.3378 | |
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| 1.2038 | 2.94 | 3000 | 1.2946 | |
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| 1.1677 | 3.04 | 3100 | 1.4802 | |
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| 1.103 | 3.14 | 3200 | 1.3580 | |
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| 1.1205 | 3.24 | 3300 | 1.3819 | |
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| 1.095 | 3.33 | 3400 | 1.4336 | |
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| 1.0896 | 3.43 | 3500 | 1.4963 | |
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| 1.0856 | 3.53 | 3600 | 1.3384 | |
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| 1.0652 | 3.63 | 3700 | 1.3583 | |
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| 1.0859 | 3.73 | 3800 | 1.4140 | |
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| 1.058 | 3.83 | 3900 | 1.2617 | |
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| 1.0724 | 3.92 | 4000 | 1.3552 | |
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| 1.0509 | 4.02 | 4100 | 1.2971 | |
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| 0.97 | 4.12 | 4200 | 1.3268 | |
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| 0.95 | 4.22 | 4300 | 1.3754 | |
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| 0.9337 | 4.32 | 4400 | 1.3687 | |
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| 0.977 | 4.41 | 4500 | 1.3613 | |
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| 0.9484 | 4.51 | 4600 | 1.5139 | |
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| 0.9739 | 4.61 | 4700 | 1.2861 | |
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| 0.955 | 4.71 | 4800 | 1.3667 | |
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| 0.9536 | 4.81 | 4900 | 1.3180 | |
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| 0.9541 | 4.9 | 5000 | 1.4611 | |
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| 0.9462 | 5.0 | 5100 | 1.4067 | |
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| 0.8728 | 5.1 | 5200 | 1.3490 | |
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| 0.8646 | 5.2 | 5300 | 1.4631 | |
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| 0.8683 | 5.3 | 5400 | 1.4978 | |
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| 0.8571 | 5.39 | 5500 | 1.5814 | |
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| 0.8475 | 5.49 | 5600 | 1.5535 | |
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| 0.8653 | 5.59 | 5700 | 1.4938 | |
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| 0.8664 | 5.69 | 5800 | 1.4141 | |
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| 0.889 | 5.79 | 5900 | 1.4487 | |
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| 0.8601 | 5.88 | 6000 | 1.4722 | |
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| 0.8645 | 5.98 | 6100 | 1.5843 | |
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| 0.785 | 6.08 | 6200 | 1.6028 | |
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| 0.7711 | 6.18 | 6300 | 1.6271 | |
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| 0.8056 | 6.28 | 6400 | 1.5399 | |
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| 0.8087 | 6.37 | 6500 | 1.4927 | |
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| 0.7859 | 6.47 | 6600 | 1.4677 | |
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| 0.7896 | 6.57 | 6700 | 1.4780 | |
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| 0.7971 | 6.67 | 6800 | 1.5110 | |
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| 0.7952 | 6.77 | 6900 | 1.5459 | |
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| 0.7971 | 6.87 | 7000 | 1.5282 | |
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| 0.7908 | 6.96 | 7100 | 1.4799 | |
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| 0.7456 | 7.06 | 7200 | 1.6487 | |
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| 0.7236 | 7.16 | 7300 | 1.6543 | |
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| 0.7484 | 7.26 | 7400 | 1.6202 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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