transformers pytorch tensorboard distilbert text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
language:
- en
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_qqp_192
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QQP
      type: glue
      args: qqp
    metrics:
    - type: accuracy
      value: 0.7887212465990601
      name: Accuracy
    - type: f1
      value: 0.7232374287195439
      name: F1
---

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# distilbert_sa_GLUE_Experiment_data_aug_qqp_192

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: 0.4878
- Accuracy: 0.7887
- F1: 0.7232
- Combined Score: 0.7560

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:|
| 0.4161        | 1.0   | 29671  | 0.4878          | 0.7887   | 0.7232 | 0.7560         |
| 0.2684        | 2.0   | 59342  | 0.5351          | 0.7965   | 0.7274 | 0.7619         |
| 0.1914        | 3.0   | 89013  | 0.6132          | 0.7992   | 0.7324 | 0.7658         |
| 0.1466        | 4.0   | 118684 | 0.6588          | 0.7999   | 0.7350 | 0.7674         |
| 0.1178        | 5.0   | 148355 | 0.7657          | 0.7983   | 0.7338 | 0.7661         |
| 0.0978        | 6.0   | 178026 | 0.7679          | 0.8040   | 0.7313 | 0.7677         |


### Framework versions

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