transformers pytorch optimum_graphcore roberta question-answering generated_from_trainer dataset:squad_v2 dataset:quoref dataset:adversarial_qa dataset:duorc license:mit model-index endpoints_compatible region:us
---
datasets:
- squad_v2
- quoref
- adversarial_qa
- duorc
license: mit
tags:
- generated_from_trainer
model-index:
- name: rob-base-gc1
  results:
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: adversarial_qa
      type: adversarial_qa
      config: adversarialQA
      split: validation
    metrics:
    - type: exact_match
      value: 42.9
      name: Exact Match
      verified: true
    - type: f1
      value: 53.8954
      name: F1
      verified: true
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad_v2
      type: squad_v2
      config: squad_v2
      split: validation
    metrics:
    - type: exact_match
      value: 79.5382
      name: Exact Match
      verified: true
    - type: f1
      value: 82.7221
      name: F1
      verified: true
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: quoref
      type: quoref
      config: default
      split: validation
    metrics:
    - type: exact_match
      value: 78.403
      name: Exact Match
      verified: true
    - type: f1
      value: 82.1408
      name: F1
      verified: true
---

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# rob-base-gc1

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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2.0
- training precision: Mixed Precision

### Training results



### Framework versions

- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1
