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---
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: 100.0
name: Exact
- type: f1
value: 100.0
name: F1
- type: total
value: 2
name: Total
- type: HasAns_exact
value: 100.0
name: Hasans_exact
- type: HasAns_f1
value: 100.0
name: Hasans_f1
- type: HasAns_total
value: 2
name: Hasans_total
- type: best_exact
value: 100.0
name: Best_exact
- type: best_exact_thresh
value: 0.967875599861145
name: Best_exact_thresh
- type: best_f1
value: 100.0
name: Best_f1
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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
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