transformers pytorch roberta text-classification generated_from_trainer dataset:glue base_model:distilbert/distilroberta-base base_model:finetune:distilbert/distilroberta-base license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: distilroberta-base
datasets:
- glue
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- text-classification
- generated_from_trainer
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:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: mrpc
      split: validation
      args: mrpc
    metrics:
    - type: accuracy
      value: 0.8455882352941176
      name: Accuracy
    - type: f1
      value: 0.8868940754039497
      name: F1
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilroberta-base-mrpc-glue-francisco-flores

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/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
