metadata

language:
  - en
license: mit
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: camembert-base-mrpc
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: GLUE MRPC
          type: glue
          args: mrpc
        metrics:
          - type: accuracy
            value: 0.8504901960784313
            name: Accuracy
          - type: f1
            value: 0.8927943760984183
            name: F1
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: glue
          type: glue
          config: mrpc
          split: validation
        metrics:
          - type: accuracy
            value: 0.8504901960784313
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.8758620689655172
            name: Precision
            verified: true
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          - type: recall
            value: 0.910394265232975
            name: Recall
            verified: true
            verifyToken: >-
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          - type: auc
            value: 0.9029062821260871
            name: AUC
            verified: true
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          - type: f1
            value: 0.8927943760984183
            name: F1
            verified: true
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          - type: loss
            value: 0.42868512868881226
            name: loss
            verified: true
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camembert-base-mrpc

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

Loss: 0.4286
Accuracy: 0.8505
F1: 0.8928
Combined Score: 0.8716

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: 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: 5.0

Training results

Framework versions

Transformers 4.18.0
Pytorch 1.10.0+cu102
Datasets 2.1.0
Tokenizers 0.11.6