metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
  - f1
widget:
  - text: on a boat trip to denmark
    example_title: Example 1
  - text: i was feeling listless from the need of new things something different
    example_title: Example 2
  - text: >-
      i know im feeling agitated as it is from a side effect of the too high
      dose
    example_title: Example 3
model-index:
  - name: distilbert-base-uncased-finetuned-emotions-dataset
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: emotion
          type: emotion
          config: split
          split: validation
          args: split
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9395
          - name: F1
            type: f1
            value: 0.9396359245863207
pipeline_tag: text-classification
language:
  - en
library_name: transformers

distilbert-base-uncased-finetuned-emotions-dataset

This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set:

Loss: 0.2428
Accuracy: 0.9395
F1: 0.9396

Model description

The model has been trained to classify text inputs into distinct emotional categories based on the fine-tuned understanding of the emotions dataset. The fine-tuned model has demonstrated high accuracy and F1 scores on the evaluation set.

Intended uses & limitations

Intended Uses

Sentiment analysis
Emotional classification in text
Emotion-based recommendation systems

Limitations

May show biases based on the training dataset
Optimized for emotional classification and may not cover nuanced emotional subtleties

Training and evaluation data

Emotions dataset with labeled emotional categories here.

The emotional categories are as follows:

LABEL_0: sadness
LABEL_1: joy
LABEL_2: love
LABEL_3: anger
LABEL_4: fear
LABEL_5: surprise

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.5929	1.0	500	0.2345	0.9185	0.9180
0.1642	2.0	1000	0.1716	0.9335	0.9342
0.1163	3.0	1500	0.1501	0.9405	0.9407
0.0911	4.0	2000	0.1698	0.933	0.9331
0.0741	5.0	2500	0.1926	0.932	0.9323
0.0559	6.0	3000	0.2033	0.935	0.9353
0.0464	7.0	3500	0.2156	0.935	0.9353
0.0335	8.0	4000	0.2354	0.9405	0.9408
0.0257	9.0	4500	0.2410	0.9395	0.9396
0.0214	10.0	5000	0.2428	0.9395	0.9396

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0