---
license: mit
base_model: microsoft/MiniLM-L12-H384-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: minilm-imdb
  results: 
  - task:
      name: text-classification
      type: text-classification
    dataset:
      name: imdb
      type: imdb
      config: default
      split: train
      args: default
    metrics:
    - name: accuracy
      type: accuracy
      value: 0.922880
    - name: f1
      type: f1
      value: 0.922831
datasets:
- imdb
language:
- en
pipeline_tag: text-classification
---

<!-- 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-imdb

This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2403
- Accuracy: 0.9229
- F1: 0.9228

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1511        | 1.0   | 293  | 0.2212          | 0.9234   | 0.9229 |
| 0.1047        | 2.0   | 586  | 0.2211          | 0.9230   | 0.9217 |
| 0.1008        | 3.0   | 879  | 0.2403          | 0.9229   | 0.9228 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0