| license: mit | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - squad_v2 | |
| - quoref | |
| - adversarial_qa | |
| - duorc | |
| model-index: | |
| - name: rob-base-superqa2 | |
| results: | |
| - task: | |
| type: question-answering | |
| name: Question Answering | |
| dataset: | |
| name: squad_v2 | |
| type: squad_v2 | |
| config: squad_v2 | |
| split: validation | |
| metrics: | |
| - name: Exact Match | |
| type: exact_match | |
| value: 79.2365 | |
| verified: true | |
| - name: F1 | |
| type: f1 | |
| value: 82.3326 | |
| verified: true | |
| - task: | |
| type: question-answering | |
| name: Question Answering | |
| dataset: | |
| name: adversarial_qa | |
| type: adversarial_qa | |
| config: adversarialQA | |
| split: test | |
| metrics: | |
| - name: Exact Match | |
| type: exact_match | |
| value: 12.4 | |
| verified: true | |
| - name: F1 | |
| type: f1 | |
| value: 12.4 | |
| verified: true | |
| - task: | |
| type: question-answering | |
| name: Question Answering | |
| dataset: | |
| name: adversarial_qa | |
| type: adversarial_qa | |
| config: adversarialQA | |
| split: validation | |
| metrics: | |
| - name: Exact Match | |
| type: exact_match | |
| value: 42.3667 | |
| verified: true | |
| - name: F1 | |
| type: f1 | |
| value: 53.3255 | |
| verified: true | |
| - task: | |
| type: question-answering | |
| name: Question Answering | |
| dataset: | |
| name: squad | |
| type: squad | |
| config: plain_text | |
| split: validation | |
| metrics: | |
| - name: Exact Match | |
| type: exact_match | |
| value: 86.1925 | |
| verified: true | |
| - name: F1 | |
| type: f1 | |
| value: 92.4306 | |
| verified: true | |
| <!-- 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. --> | |
| # rob-base-superqa2 | |
| This model is a fine-tuned version of [roberta-base](https://huggingface.co./roberta-base) on the None dataset. | |
| ## 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: 0.0001 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - distributed_type: multi-GPU | |
| - num_devices: 8 | |
| - total_train_batch_size: 256 | |
| - total_eval_batch_size: 256 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 2.0 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.21.1 | |
| - Pytorch 1.11.0a0+gita4c10ee | |
| - Datasets 2.4.0 | |
| - Tokenizers 0.12.1 | |