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tinybert-emotion-balanced

Text Classification
Transformers
PyTorch
ONNX
Safetensors
bert
Generated from Trainer
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1
tinybert-emotion-balanced
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- AdamCodd/emotion-balanced
metrics:
- accuracy
- f1
- recall
- precision
widget:
- text: "He looked out of the rain-streaked window, lost in thought, the faintest hint of melancholy in his eyes, as he remembered moments from a distant past."
  example_title: "Sadness"
- text: "As she strolled through the park, a soft smile played on her lips, and her heart felt lighter with each step, appreciating the simple beauty of nature."
  example_title: "Joy"
- text: "Their fingers brushed lightly as they exchanged a knowing glance, a subtle connection that spoke volumes about the deep affection they held for each other."
  example_title: "Love"
- text: "She clenched her fists and took a deep breath, trying to suppress the simmering frustration that welled up when her ideas were dismissed without consideration."
  example_title: "Anger"
- text: "In the quiet of the night, the gentle rustling of leaves outside her window sent shivers down her spine, leaving her feeling uneasy and vulnerable."
  example_title: "Fear"
- text: "Upon opening the old dusty book, a delicate, hand-painted map fell out, revealing hidden treasures she never expected to find."
  example_title: "Surprise"
base_model: distilbert-base-uncased
model-index:
- name: distilbert-base-uncased-finetuned-emotion-balanced
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      args: default
    metrics:
    - type: accuracy
      value: 0.9354
      name: Accuracy
    - type: loss
      value: 0.1809
      name: Loss
    - type: f1
      value: 0.9354946613311768
      name: F1
---

# tinybert-emotion

This model is a fine-tuned version of [bert-tiny](prajjwal1/bert-tiny) on the [emotion balanced dataset](https://huggingface.co/datasets/AdamCodd/emotion-balanced).
It achieves the following results on the evaluation set:
- Loss: 0.1809
- Accuracy: 0.9354

## Model description

TinyBERT is 7.5 times smaller and 9.4 times faster on inference compared to its teacher BERT model (while DistilBERT is 40% smaller and 1.6 times faster than BERT). The model has been trained on 89_754 examples split into train, validation and test. Each label was perfectly balanced in each split.

## Intended uses & limitations

This model is not as accurate as the [distilbert-emotion-balanced](AdamCodd/distilbert-base-uncased-finetuned-emotion-balanced) since speed was the focus, so it can misinterpret complex sentences. Despite this, its performance is quite good and should be more than enough for most use cases.

Usage:
```python
from transformers import pipeline

# Create the pipeline
emotion_classifier = pipeline('text-classification', model='AdamCodd/tinybert-emotion-balanced')

# Now you can use the pipeline to classify emotions
result = emotion_classifier("We are delighted that you will be coming to visit us. It will be so nice to have you here.")
print(result)
#[{'label': 'joy', 'score': 0.9895486831665039}]
```
This model faces challenges in accurately categorizing negative sentences, as well as those containing elements of sarcasm or irony. These limitations are largely attributable to TinyBERT's constrained capabilities in semantic understanding. Although the model is generally proficient in emotion detection tasks, it may lack the nuance necessary for interpreting complex emotional nuances.

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 1270
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 10
- weight_decay: 0.01

### Training results

              precision    recall  f1-score   support

     sadness     0.9733    0.9245    0.9482      1496
         joy     0.9651    0.8864    0.9240      1496
        love     0.9127    0.9786    0.9445      1496
       anger     0.9479    0.9365    0.9422      1496
        fear     0.9213    0.9004    0.9108      1496
    surprise     0.9016    0.9866    0.9422      1496

    accuracy                         0.9355      8976
    macro avg    0.9370    0.9355    0.9353      8976
    weighted avg 0.9370    0.9355    0.9353      8976
    
    test_acc:     0.9354946613311768
    test_loss:    0.1809326708316803

### Framework versions

- Transformers 4.33.0
- Pytorch lightning 2.0.8
- Tokenizers 0.13.3

If you want to support me, you can [here](https://ko-fi.com/adamcodd).