---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_qqp_256
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE QQP
      type: glue
      args: qqp
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7887954489240663
    - name: F1
      type: f1
      value: 0.7301115711621732
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilbert_sa_GLUE_Experiment_data_aug_qqp_256

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5126
- Accuracy: 0.7888
- F1: 0.7301
- Combined Score: 0.7595

## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:|
| 0.3952        | 1.0   | 29671  | 0.5126          | 0.7888   | 0.7301 | 0.7595         |
| 0.2233        | 2.0   | 59342  | 0.5941          | 0.7960   | 0.7346 | 0.7653         |
| 0.147         | 3.0   | 89013  | 0.6603          | 0.7997   | 0.7340 | 0.7668         |
| 0.1067        | 4.0   | 118684 | 0.7091          | 0.8012   | 0.7376 | 0.7694         |
| 0.082         | 5.0   | 148355 | 0.8757          | 0.8000   | 0.7377 | 0.7688         |
| 0.0652        | 6.0   | 178026 | 0.8332          | 0.8044   | 0.7379 | 0.7711         |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2