---
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: 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