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_qqp_96
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE QQP
      type: glue
      config: qqp
      split: validation
      args: qqp
    metrics:
    - type: accuracy
      value: 0.7756863715063071
      name: Accuracy
    - type: f1
      value: 0.6671804469888803
      name: F1
---

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# distilbert_sa_GLUE_Experiment_qqp_96

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: 0.4818
- Accuracy: 0.7757
- F1: 0.6672
- Combined Score: 0.7214

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5528        | 1.0   | 1422  | 0.5115          | 0.7528   | 0.6284 | 0.6906         |
| 0.4965        | 2.0   | 2844  | 0.4960          | 0.7614   | 0.6420 | 0.7017         |
| 0.4769        | 3.0   | 4266  | 0.4904          | 0.7650   | 0.6382 | 0.7016         |
| 0.4619        | 4.0   | 5688  | 0.4901          | 0.7680   | 0.6526 | 0.7103         |
| 0.4489        | 5.0   | 7110  | 0.4844          | 0.7709   | 0.6663 | 0.7186         |
| 0.4366        | 6.0   | 8532  | 0.4860          | 0.7721   | 0.6712 | 0.7217         |
| 0.425         | 7.0   | 9954  | 0.4860          | 0.7747   | 0.6636 | 0.7192         |
| 0.414         | 8.0   | 11376 | 0.4818          | 0.7757   | 0.6672 | 0.7214         |
| 0.4027        | 9.0   | 12798 | 0.4871          | 0.7786   | 0.6722 | 0.7254         |
| 0.3926        | 10.0  | 14220 | 0.4919          | 0.7749   | 0.6932 | 0.7340         |
| 0.3824        | 11.0  | 15642 | 0.4890          | 0.7801   | 0.6823 | 0.7312         |
| 0.3718        | 12.0  | 17064 | 0.4981          | 0.7801   | 0.6970 | 0.7385         |
| 0.3629        | 13.0  | 18486 | 0.4989          | 0.7805   | 0.6968 | 0.7386         |


### Framework versions

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