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
tags:
- generated_from_trainer
model-index:
- name: distilbert_sa_GLUE_Experiment_logit_kd_data_aug_sst2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE SST2
      type: glue
      args: sst2
    metrics:
    - type: accuracy
      value: 0.8635321100917431
      name: Accuracy
---

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# distilbert_sa_GLUE_Experiment_logit_kd_data_aug_sst2

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4215
- Accuracy: 0.8635

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.434         | 1.0   | 4374  | 0.6108          | 0.8222   |
| 0.2568        | 2.0   | 8748  | 0.5465          | 0.8314   |
| 0.207         | 3.0   | 13122 | 0.5067          | 0.8463   |
| 0.1819        | 4.0   | 17496 | 0.4734          | 0.8544   |
| 0.1666        | 5.0   | 21870 | 0.4785          | 0.8578   |
| 0.1563        | 6.0   | 26244 | 0.4539          | 0.8589   |
| 0.1492        | 7.0   | 30618 | 0.4600          | 0.8589   |
| 0.1436        | 8.0   | 34992 | 0.4445          | 0.8647   |
| 0.1394        | 9.0   | 39366 | 0.4270          | 0.8727   |
| 0.1361        | 10.0  | 43740 | 0.4524          | 0.8601   |
| 0.1334        | 11.0  | 48114 | 0.4244          | 0.8693   |
| 0.1313        | 12.0  | 52488 | 0.4469          | 0.8635   |
| 0.1292        | 13.0  | 56862 | 0.4556          | 0.8498   |
| 0.1277        | 14.0  | 61236 | 0.4257          | 0.8635   |
| 0.1263        | 15.0  | 65610 | 0.4392          | 0.8567   |
| 0.1251        | 16.0  | 69984 | 0.4215          | 0.8635   |
| 0.124         | 17.0  | 74358 | 0.4289          | 0.8578   |
| 0.123         | 18.0  | 78732 | 0.4448          | 0.8601   |
| 0.1222        | 19.0  | 83106 | 0.4562          | 0.8555   |
| 0.1214        | 20.0  | 87480 | 0.4377          | 0.8544   |
| 0.1207        | 21.0  | 91854 | 0.4563          | 0.8555   |


### Framework versions

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