metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
  - f1
model-index:
  - name: DistilBERT-finetuned-on-emotion
    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: 0.9235
          - name: F1
            type: f1
            value: 0.9234955371382243
widget:
  - text: >-
      The gentle touch of your hand on mine is a silent promise that echoes
      through the corridors of my heart.
  - text: ' Walking through the dusty attic, I stumbled upon a hidden door. With a mix of trepidation and excitement, I pushed it open, expecting cobwebs and forgotten junk. Instead, a flood of sunlight revealed a secret garden, blooming with vibrant flowers and buzzing with life. My jaw dropped in pure astonishment.'
  - text: >-
      The rain mirrored the tears I couldn't stop, each drop a tiny echo of the
      ache in my heart. The world seemed muted, colors drained, and a heavy
      weight settled upon my soul.
  - text: >-
      Staring at your pic, smiling. It feels like I am living this night awake
      in ur dreams. I went through our chats all of them n each word kissed my
      heart with gratitude. Suddenly, I felt like I am choking. my eyes welling
      up .my breath turned warm n deep n I even felt some chill on my skin.

DistilBERT-finetuned-on-emotion

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: 0.2180
Accuracy: 0.9235
F1: 0.9235

Model description

DiestilBERT is fine-tuned on emotions dataset. Click the following link to see how the model works: https://huggingface.co/spaces/Rahmat82/emotions_classifier

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: 2e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.8046	1.0	250	0.3115	0.9085	0.9081
0.2405	2.0	500	0.2180	0.9235	0.9235

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0