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 sentence
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: 0.9521
name: Accuracy
- type: loss
value: 0.1216
name: Loss
- type: f1
value: 0.9520944952964783
name: F1
distilbert-emotion
Reupload [10/02/23] : The model has been retrained using identical hyperparameters, but this time on an even more pristine dataset, free of certain scraping artifacts. Remarkably, it maintains the same level of accuracy and loss while demonstrating superior generalization capabilities.
This model is a fine-tuned version of distilbert-base-uncased on the emotion balanced dataset. It achieves the following results on the evaluation set:
- Loss: 0.1216
- Accuracy: 0.9521
Model description
This emotion classifier has been trained on 89_754 examples split into train, validation and test. Each label was perfectly balanced in each split.
Intended uses & limitations
Usage:
from transformers import pipeline
# Create the pipeline
emotion_classifier = pipeline('text-classification', model='AdamCodd/distilbert-base-uncased-finetuned-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': 0.9983291029930115}]
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 DistilBERT'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: 3
- weight_decay: 0.01
Training results
precision recall f1-score support
sadness 0.9882 0.9485 0.9679 1496
joy 0.9956 0.9057 0.9485 1496
love 0.9256 0.9980 0.9604 1496
anger 0.9628 0.9519 0.9573 1496
fear 0.9348 0.9098 0.9221 1496
surprise 0.9160 0.9987 0.9555 1496
accuracy 0.9521 8976
macro avg 0.9538 0.9521 0.9520 8976
weighted avg 0.9538 0.9521 0.9520 8976
test_acc: 0.9520944952964783
test_loss: 0.121663898229599
Framework versions
- Transformers 4.33.2
- Pytorch lightning 2.0.9
- Tokenizers 0.13.3
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