contradiction entailment neutral
---
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: tiny-bert-mnli-distilled
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      args: mnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5818644931227712
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# tiny-bert-mnli-distilled

It achieves the following results on the evaluation set:
- Loss: 1.5018
- Accuracy: 0.5819
- F1 score: 0.5782
- Precision score: 0.6036
- Metric recall: 0.5819

## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 32
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 score | Precision score | Metric recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:-------------:|
| 1.4475        | 1.0   | 614  | 1.4296          | 0.4521   | 0.4070   | 0.5621          | 0.4521        |
| 1.3354        | 2.0   | 1228 | 1.4320          | 0.4805   | 0.4579   | 0.5276          | 0.4805        |
| 1.2244        | 3.0   | 1842 | 1.4786          | 0.5699   | 0.5602   | 0.5865          | 0.5699        |
| 1.1416        | 4.0   | 2456 | 1.5018          | 0.5819   | 0.5782   | 0.6036          | 0.5819        |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.9.1
- Datasets 2.1.0
- Tokenizers 0.11.6
