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--- |
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license: mit |
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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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- quoref |
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- adversarial_qa |
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- duorc |
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model-index: |
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- name: rob-base-gc1 |
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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: adversarial_qa |
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type: adversarial_qa |
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config: adversarialQA |
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split: validation |
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metrics: |
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- name: Exact Match |
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type: exact_match |
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value: 42.9 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 53.8954 |
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verified: true |
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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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config: squad_v2 |
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split: validation |
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metrics: |
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- name: Exact Match |
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type: exact_match |
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value: 79.5382 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 82.7221 |
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verified: true |
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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: quoref |
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type: quoref |
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config: default |
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split: validation |
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metrics: |
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- name: Exact Match |
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type: exact_match |
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value: 78.403 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 82.1408 |
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verified: true |
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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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# rob-base-gc1 |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
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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: 0.0001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: IPU |
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- gradient_accumulation_steps: 64 |
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- total_train_batch_size: 256 |
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- total_eval_batch_size: 20 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 2.0 |
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- training precision: Mixed Precision |
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### Training results |
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### Framework versions |
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- Transformers 4.20.0 |
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- Pytorch 1.10.0+cpu |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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