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_add_GLUE_Experiment_mnli_384
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE MNLI
      type: glue
      config: mnli
      split: validation_matched
      args: mnli
    metrics:
    - type: accuracy
      value: 0.5652969894222946
      name: Accuracy
---

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# distilbert_add_GLUE_Experiment_mnli_384

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9341
- Accuracy: 0.5653

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.074         | 1.0   | 1534  | 1.0374          | 0.4592   |
| 1.0289        | 2.0   | 3068  | 1.0214          | 0.4736   |
| 1.0052        | 3.0   | 4602  | 0.9957          | 0.4889   |
| 0.9869        | 4.0   | 6136  | 0.9924          | 0.5111   |
| 0.9709        | 5.0   | 7670  | 0.9683          | 0.5246   |
| 0.9562        | 6.0   | 9204  | 0.9628          | 0.5295   |
| 0.9403        | 7.0   | 10738 | 0.9524          | 0.5379   |
| 0.9244        | 8.0   | 12272 | 0.9547          | 0.5309   |
| 0.9071        | 9.0   | 13806 | 0.9358          | 0.5563   |
| 0.8914        | 10.0  | 15340 | 0.9286          | 0.5586   |
| 0.8771        | 11.0  | 16874 | 0.9384          | 0.5651   |
| 0.863         | 12.0  | 18408 | 0.9361          | 0.5616   |
| 0.849         | 13.0  | 19942 | 0.9401          | 0.5606   |
| 0.8346        | 14.0  | 21476 | 0.9441          | 0.5646   |
| 0.8199        | 15.0  | 23010 | 0.9276          | 0.5677   |
| 0.8048        | 16.0  | 24544 | 0.9444          | 0.5656   |
| 0.7883        | 17.0  | 26078 | 0.9477          | 0.5653   |
| 0.7707        | 18.0  | 27612 | 0.9491          | 0.5745   |
| 0.7543        | 19.0  | 29146 | 0.9613          | 0.5825   |
| 0.7371        | 20.0  | 30680 | 0.9645          | 0.5728   |


### Framework versions

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