File size: 5,936 Bytes
72949f0 7df7b96 889dcea 7df7b96 2774915 2f9819b 77821f4 fb956d8 103c959 5c16e3c 17d8ab8 36ea08b 735c2d8 607460a 7b8e82d 72949f0 7df7b96 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 |
---
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
- type: best_f1_thresh
value: 0.967875599861145
name: Best_f1_thresh
- type: total_time_in_seconds
value: 0.02787837800019588
name: Total_time_in_seconds
---
<!-- 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
|