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rob-base-gc1

Question Answering Transformers PyTorch Graphcore
roberta generated_from_trainer Eval Results Inference Endpoints
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metadata
license: mit
tags:
  - generated_from_trainer
datasets:
  - squad_v2
  - quoref
  - adversarial_qa
  - duorc
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:
          - name: Exact Match
            type: exact_match
            value: 42.9
            verified: true
          - name: F1
            type: f1
            value: 53.8954
            verified: true
      - 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: 79.5382
            verified: true
          - name: F1
            type: f1
            value: 82.7221
            verified: true
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: quoref
          type: quoref
          config: default
          split: validation
        metrics:
          - name: Exact Match
            type: exact_match
            value: 78.403
            verified: true
          - name: F1
            type: f1
            value: 82.1408
            verified: true

rob-base-gc1

This model is a fine-tuned version of 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