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---
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
should probably proofread and complete it, then remove this comment. -->
# 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
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