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Add evaluation results on the qqp config of glue
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metadata
language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
  - fnet-bert-base-comparison
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: bert-base-cased-finetuned-qqp
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE QQP
          type: glue
          args: qqp
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9083848627256987
          - name: F1
            type: f1
            value: 0.8767633750332712
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: glue
          type: glue
          config: qqp
          split: validation
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9083106604006925
            verified: true
          - name: Precision
            type: precision
            value: 0.8684245220830586
            verified: true
          - name: Recall
            type: recall
            value: 0.885052065838092
            verified: true
          - name: AUC
            type: auc
            value: 0.966124561811495
            verified: true
          - name: F1
            type: f1
            value: 0.876659457660955
            verified: true
          - name: loss
            type: loss
            value: 0.3751780092716217
            verified: true

bert-base-cased-finetuned-qqp

This model is a fine-tuned version of bert-base-cased on the GLUE QQP dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3752
  • Accuracy: 0.9084
  • F1: 0.8768
  • Combined Score: 0.8926

The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

This model is trained using the run_glue script. The following command was used:

#!/usr/bin/bash

python ../run_glue.py \\n  --model_name_or_path bert-base-cased \\n  --task_name qqp \\n  --do_train \\n  --do_eval \\n  --max_seq_length 512 \\n  --per_device_train_batch_size 16 \\n  --learning_rate 2e-5 \\n  --num_train_epochs 3 \\n  --output_dir bert-base-cased-finetuned-qqp \\n  --push_to_hub \\n  --hub_strategy all_checkpoints \\n  --logging_strategy epoch \\n  --save_strategy epoch \\n  --evaluation_strategy epoch \\n```


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.308         | 1.0   | 22741 | 0.2548          | 0.8925   | 0.8556 | 0.8740         |
| 0.201         | 2.0   | 45482 | 0.2881          | 0.9032   | 0.8698 | 0.8865         |
| 0.1416        | 3.0   | 68223 | 0.3752          | 0.9084   | 0.8768 | 0.8926         |


### Framework versions

- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3