metadata

library_name: transformers
license: apache-2.0
base_model: bert-base-multilingual-uncased
tags:
  - generated_from_trainer
datasets:
  - super_glue
metrics:
  - accuracy
  - f1
model-index:
  - name: mbert-multirc
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: super_glue
          type: super_glue
          config: multirc
          split: validation
          args: multirc
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.5759075907590759
          - name: F1
            type: f1
            value: 0.5048127206005825

mbert-multirc

This model is a fine-tuned version of bert-base-multilingual-uncased on the super_glue dataset. It achieves the following results on the evaluation set:

Loss: 0.6812
Accuracy: 0.5759
F1: 0.5048

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: 1e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.6862	1.0	1703	0.6812	0.5759	0.5048

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3