---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
base_model: google/bert_uncased_L-2_H-128_A-2
model-index:
- name: BERT-tiny-emotion-intent
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: default
      split: train
      args: default
    metrics:
    - type: accuracy
      value: 0.91
      name: Accuracy
---

<!-- 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. -->

# BERT-tiny-emotion-intent

This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3620
- Accuracy: 0.91

## 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: 16
- eval_batch_size: 16
- seed: 33
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2603        | 1.0   | 1000  | 0.7766          | 0.7815   |
| 0.5919        | 2.0   | 2000  | 0.4117          | 0.884    |
| 0.367         | 3.0   | 3000  | 0.3188          | 0.8995   |
| 0.2848        | 4.0   | 4000  | 0.2928          | 0.8985   |
| 0.2395        | 5.0   | 5000  | 0.2906          | 0.898    |
| 0.2094        | 6.0   | 6000  | 0.2887          | 0.907    |
| 0.1884        | 7.0   | 7000  | 0.2831          | 0.9065   |
| 0.1603        | 8.0   | 8000  | 0.3044          | 0.9065   |
| 0.1519        | 9.0   | 9000  | 0.3124          | 0.9095   |
| 0.1291        | 10.0  | 10000 | 0.3256          | 0.9065   |
| 0.1179        | 11.0  | 11000 | 0.3651          | 0.9035   |
| 0.1091        | 12.0  | 12000 | 0.3620          | 0.91     |
| 0.0977        | 13.0  | 13000 | 0.3992          | 0.907    |
| 0.0914        | 14.0  | 14000 | 0.4285          | 0.908    |
| 0.0876        | 15.0  | 15000 | 0.4268          | 0.9055   |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1