metadata

license: apache-2.0
base_model: distilroberta-base
tags:
  - text-classification
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
widget:
  - text:
      - >-
        The weather is sunny, and the sky is clear, making it a perfect day for
        outdoor activities.
      - >-
        Despite the heavy rain, the children enjoyed playing in the puddles and
        splashing each other.
    example_title: Not Equivalent
  - text:
      - He is an extremely kind individual and is always ready to assist others.
      - >-
        His character is very gentle, and he never hesitates to provide aid to
        those around him.
    example_title: Equivalent
model-index:
  - name: distilroberta-base-mrpc-glue-francisco-flores
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: glue
          type: glue
          config: mrpc
          split: validation
          args: mrpc
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8455882352941176
          - name: F1
            type: f1
            value: 0.8868940754039497

distilroberta-base-mrpc-glue-francisco-flores

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

Loss: 0.7184
Accuracy: 0.8456
F1: 0.8869

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.338	1.09	500	0.7782	0.8382	0.8896
0.3216	2.18	1000	0.7184	0.8456	0.8869
0.1861	3.27	1500	1.1095	0.8358	0.8874
0.1101	4.36	2000	1.3526	0.8260	0.8799
0.0572	5.45	2500	1.2464	0.8260	0.8757
0.0443	6.54	3000	1.2194	0.8407	0.8866
0.0321	7.63	3500	1.3519	0.8333	0.8803
0.0146	8.71	4000	1.4999	0.8309	0.8840
0.0082	9.8	4500	1.4908	0.8333	0.8840

Framework versions

Transformers 4.33.1
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3