negative positive
---
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: minilm-l12-h384-sst2-distilled
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      args: sst2
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9220183486238532
---

<!-- 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. -->

# minilm-l12-h384-sst2-distilled

This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5417
- Accuracy: 0.9220

## 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: 0.0001400785945474408
- train_batch_size: 512
- eval_batch_size: 512
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2689        | 1.0   | 132  | 0.7102          | 0.8979   |
| 0.8295        | 2.0   | 264  | 0.5669          | 0.9117   |
| 0.5059        | 3.0   | 396  | 0.5545          | 0.9220   |
| 0.3722        | 4.0   | 528  | 0.5378          | 0.9209   |
| 0.2924        | 5.0   | 660  | 0.5417          | 0.9220   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
