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_logit_kd_data_aug_mrpc
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE MRPC
      type: glue
      args: mrpc
    metrics:
    - type: accuracy
      value: 0.8946078431372549
      name: Accuracy
    - type: f1
      value: 0.916504854368932
      name: F1
---

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# distilbert_sa_GLUE_Experiment_logit_kd_data_aug_mrpc

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3959
- Accuracy: 0.8946
- F1: 0.9165
- Combined Score: 0.9056

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.4396        | 1.0   | 980   | 0.4005          | 0.9975   | 0.9982 | 0.9979         |
| 0.4146        | 2.0   | 1960  | 0.3981          | 0.9853   | 0.9892 | 0.9872         |
| 0.413         | 3.0   | 2940  | 0.3971          | 1.0      | 1.0    | 1.0            |
| 0.4119        | 4.0   | 3920  | 0.3968          | 0.9902   | 0.9928 | 0.9915         |
| 0.4114        | 5.0   | 4900  | 0.3966          | 1.0      | 1.0    | 1.0            |
| 0.4112        | 6.0   | 5880  | 0.3966          | 0.9951   | 0.9964 | 0.9958         |
| 0.4108        | 7.0   | 6860  | 0.3966          | 1.0      | 1.0    | 1.0            |
| 0.4107        | 8.0   | 7840  | 0.3962          | 0.9877   | 0.9910 | 0.9894         |
| 0.4105        | 9.0   | 8820  | 0.3962          | 0.9902   | 0.9928 | 0.9915         |
| 0.4104        | 10.0  | 9800  | 0.3967          | 0.9020   | 0.9228 | 0.9124         |
| 0.4102        | 11.0  | 10780 | 0.3964          | 0.8971   | 0.9186 | 0.9078         |
| 0.4102        | 12.0  | 11760 | 0.3963          | 0.9975   | 0.9982 | 0.9979         |
| 0.4103        | 13.0  | 12740 | 0.3962          | 0.8431   | 0.8704 | 0.8568         |
| 0.4102        | 14.0  | 13720 | 0.3967          | 0.7966   | 0.8253 | 0.8109         |
| 0.4101        | 15.0  | 14700 | 0.3962          | 0.8971   | 0.9186 | 0.9078         |
| 0.4101        | 16.0  | 15680 | 0.3959          | 0.8946   | 0.9165 | 0.9056         |
| 0.41          | 17.0  | 16660 | 0.3963          | 0.8848   | 0.9080 | 0.8964         |
| 0.41          | 18.0  | 17640 | 0.3960          | 0.8676   | 0.8929 | 0.8803         |
| 0.41          | 19.0  | 18620 | 0.3962          | 0.8186   | 0.8471 | 0.8329         |
| 0.4101        | 20.0  | 19600 | 0.3959          | 0.8848   | 0.9080 | 0.8964         |
| 0.41          | 21.0  | 20580 | 0.3959          | 0.8799   | 0.9037 | 0.8918         |


### Framework versions

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