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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: roberta-finetuned-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: 0.9603068232536316 |
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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: 0.9603068232536316 |
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name: Best_f1_thresh |
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- type: total_time_in_seconds |
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value: 0.036892927000735654 |
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name: Total_time_in_seconds |
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- type: samples_per_second |
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value: 54.21093316776193 |
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name: Samples_per_second |
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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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# roberta-finetuned-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: 0.8582 |
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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: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 512 |
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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: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.9129 | 0.2 | 100 | 1.4700 | |
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| 1.4395 | 0.39 | 200 | 1.2407 | |
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| 1.2356 | 0.59 | 300 | 1.0325 | |
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| 1.1284 | 0.78 | 400 | 0.9750 | |
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| 1.0821 | 0.98 | 500 | 0.9345 | |
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| 0.9978 | 1.18 | 600 | 0.9893 | |
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| 0.9697 | 1.37 | 700 | 0.9300 | |
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| 0.9455 | 1.57 | 800 | 0.9351 | |
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| 0.9322 | 1.76 | 900 | 0.9451 | |
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| 0.9269 | 1.96 | 1000 | 0.9064 | |
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| 0.9105 | 2.16 | 1100 | 0.8837 | |
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| 0.8805 | 2.35 | 1200 | 0.8876 | |
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| 0.8703 | 2.55 | 1300 | 0.9853 | |
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| 0.8699 | 2.75 | 1400 | 0.9235 | |
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| 0.8633 | 2.94 | 1500 | 0.8930 | |
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| 0.828 | 3.14 | 1600 | 0.8582 | |
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| 0.8284 | 3.33 | 1700 | 0.9203 | |
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| 0.8076 | 3.53 | 1800 | 0.8866 | |
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| 0.7805 | 3.73 | 1900 | 0.9099 | |
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| 0.7974 | 3.92 | 2000 | 0.8746 | |
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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 2.14.5 |
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- Tokenizers 0.14.1 |
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