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 on the emotion balanced dataset. 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 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:
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
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