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rob-base-superqa2

Question Answering
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rob-base-superqa2
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---
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