metadata

tags:
  - generated_from_trainer
datasets:
  - boolq
metrics:
  - accuracy
model-index:
  - name: MiniLMv2-L6-H768-distilled-from-RoBERTa-Large_boolq
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: boolq
          type: boolq
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7379204892966361

MiniLMv2-L6-H768-distilled-from-RoBERTa-Large_boolq

This model is a fine-tuned version of nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large on the boolq dataset. It achieves the following results on the evaluation set:

Loss: 0.5417
Accuracy: 0.7379

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: 32
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

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.85	250	0.6579	0.6190
0.6352	1.69	500	0.5907	0.6841
0.6352	2.54	750	0.5613	0.7196
0.535	3.39	1000	0.5444	0.7373
0.535	4.24	1250	0.5417	0.7379

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.14.4
Tokenizers 0.13.3