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---
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: roberta-finetuned-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: 0.9603068232536316
name: Best_exact_thresh
- type: best_f1
value: 100.0
name: Best_f1
- type: best_f1_thresh
value: 0.9603068232536316
name: Best_f1_thresh
- type: total_time_in_seconds
value: 0.036892927000735654
name: Total_time_in_seconds
- type: samples_per_second
value: 54.21093316776193
name: Samples_per_second
---
<!-- 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. -->
# roberta-finetuned-squad_v2
This model was trained from scratch on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8582
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9129 | 0.2 | 100 | 1.4700 |
| 1.4395 | 0.39 | 200 | 1.2407 |
| 1.2356 | 0.59 | 300 | 1.0325 |
| 1.1284 | 0.78 | 400 | 0.9750 |
| 1.0821 | 0.98 | 500 | 0.9345 |
| 0.9978 | 1.18 | 600 | 0.9893 |
| 0.9697 | 1.37 | 700 | 0.9300 |
| 0.9455 | 1.57 | 800 | 0.9351 |
| 0.9322 | 1.76 | 900 | 0.9451 |
| 0.9269 | 1.96 | 1000 | 0.9064 |
| 0.9105 | 2.16 | 1100 | 0.8837 |
| 0.8805 | 2.35 | 1200 | 0.8876 |
| 0.8703 | 2.55 | 1300 | 0.9853 |
| 0.8699 | 2.75 | 1400 | 0.9235 |
| 0.8633 | 2.94 | 1500 | 0.8930 |
| 0.828 | 3.14 | 1600 | 0.8582 |
| 0.8284 | 3.33 | 1700 | 0.9203 |
| 0.8076 | 3.53 | 1800 | 0.8866 |
| 0.7805 | 3.73 | 1900 | 0.9099 |
| 0.7974 | 3.92 | 2000 | 0.8746 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|