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_add_GLUE_Experiment_qqp_256
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QQP
      type: glue
      config: qqp
      split: validation
      args: qqp
    metrics:
    - type: accuracy
      value: 0.8135790254761316
      name: Accuracy
    - type: f1
      value: 0.7425272435349981
      name: F1
---

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# distilbert_add_GLUE_Experiment_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: 0.4273
- Accuracy: 0.8136
- F1: 0.7425
- Combined Score: 0.7781

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5648        | 1.0   | 1422  | 0.5394          | 0.7316   | 0.6540 | 0.6928         |
| 0.5038        | 2.0   | 2844  | 0.5093          | 0.7496   | 0.6564 | 0.7030         |
| 0.4837        | 3.0   | 4266  | 0.4952          | 0.7623   | 0.6625 | 0.7124         |
| 0.4624        | 4.0   | 5688  | 0.4777          | 0.7739   | 0.6844 | 0.7292         |
| 0.4197        | 5.0   | 7110  | 0.4541          | 0.7925   | 0.6939 | 0.7432         |
| 0.3693        | 6.0   | 8532  | 0.4539          | 0.8027   | 0.7012 | 0.7519         |
| 0.3214        | 7.0   | 9954  | 0.4273          | 0.8136   | 0.7425 | 0.7781         |
| 0.2804        | 8.0   | 11376 | 0.4547          | 0.8187   | 0.7344 | 0.7765         |
| 0.2463        | 9.0   | 12798 | 0.4779          | 0.8227   | 0.7478 | 0.7852         |
| 0.2177        | 10.0  | 14220 | 0.5060          | 0.8256   | 0.7510 | 0.7883         |
| 0.1933        | 11.0  | 15642 | 0.5020          | 0.8272   | 0.7587 | 0.7929         |
| 0.1741        | 12.0  | 17064 | 0.5385          | 0.8304   | 0.7604 | 0.7954         |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
