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_384
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QQP
      type: glue
      config: qqp
      split: validation
      args: qqp
    metrics:
    - type: accuracy
      value: 0.8095226317091269
      name: Accuracy
    - type: f1
      value: 0.737194143944306
      name: F1
---

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# distilbert_add_GLUE_Experiment_qqp_384

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: 0.4096
- Accuracy: 0.8095
- F1: 0.7372
- Combined Score: 0.7734

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5518        | 1.0   | 1422  | 0.5289          | 0.7376   | 0.6535 | 0.6955         |
| 0.4901        | 2.0   | 2844  | 0.4655          | 0.7772   | 0.6744 | 0.7258         |
| 0.4098        | 3.0   | 4266  | 0.4096          | 0.8095   | 0.7372 | 0.7734         |
| 0.3273        | 4.0   | 5688  | 0.4343          | 0.8211   | 0.7536 | 0.7873         |
| 0.2681        | 5.0   | 7110  | 0.4322          | 0.8286   | 0.7519 | 0.7902         |
| 0.223         | 6.0   | 8532  | 0.4789          | 0.8301   | 0.7502 | 0.7901         |
| 0.1883        | 7.0   | 9954  | 0.4715          | 0.8329   | 0.7663 | 0.7996         |
| 0.1603        | 8.0   | 11376 | 0.5090          | 0.8346   | 0.7577 | 0.7961         |


### Framework versions

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