---
pipeline_tag: text-classification
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
datasets:
- dair-ai/emotion
language:
- en
model-index:
- name: results
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# results

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1699
- Accuracy: 0.941

## Model Description

This model is a fine-tuned version of [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased), tailored for emotion recognition in text. It classifies input text into one of six emotion categories: sadness, joy, love, anger, fear, and surprise. The fine-tuning was performed on the [dair-ai/emotion](https://huggingface.co/datasets/dair-ai/emotion) dataset, which includes 20,000 labeled text-emotion pairs. DistilBERT, being a smaller and faster variant of BERT, ensures this model is efficient while delivering robust performance for emotion classification tasks.

- **Model Type**: Text Classification
- **Base Model**: [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased)
- **Fine-Tuning Task**: Emotion Recognition (6 classes)
- **Languages**: English
- **License**: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)

## Intended Uses & Limitations

### Intended Uses
- **Emotion Classification**: Classify text into one of six emotions: sadness, joy, love, anger, fear, or surprise.
- **Sentiment Analysis**: Infer sentiment (e.g., joy as positive, anger as negative) based on predicted emotions, though not explicitly trained for this purpose.
- **Chatbots and Virtual Assistants**: Enhance conversational AI by detecting user emotions for empathetic responses.
- **Content Moderation**: Identify content with strong emotions like anger or fear for moderation purposes.

### Limitations
- **Emotion Granularity**: Restricted to six emotions, potentially missing nuanced or complex emotional states.
- **Contextual Understanding**: May struggle with sarcasm, irony, or emotions requiring deeper context.
- **Language**: Trained on English text only, with limited performance on other languages.
- **Dataset Bias**: Performance may reflect biases in the training data, such as underrepresentation of certain emotional expressions.
- **Short Texts**: Suboptimal performance on very short inputs (e.g., single words) due to limited context.

## Training and Evaluation Data

The model was fine-tuned on the [dair-ai/emotion](https://huggingface.co/datasets/dair-ai/emotion) dataset, comprising 20,000 English text samples labeled with one of six emotions:
- **0**: sadness
- **1**: joy
- **2**: love
- **3**: anger
- **4**: fear
- **5**: surprise

The dataset is divided as follows:
- **Training Set**: 16,000 samples
- **Validation Set**: 2,000 samples
- **Test Set**: 2,000 samples

The dataset is balanced across the six emotion classes, promoting effective learning for each category.

## Training Procedure

### Preprocessing
- **Tokenization**: Text was tokenized using the DistilBERT tokenizer, with a maximum sequence length of 512 tokens. Padding and truncation ensured uniform input sizes.
- **Data Formatting**: Converted to PyTorch tensors for training compatibility.

## Demo
Try the model in action [here](https://huggingface.co/spaces/YonasMersha/emotion-classifier).

### Training Hyperparameters
Fine-tuning was conducted using the Hugging Face `Trainer` API with:
- **Epochs**: 3
- **Batch Size**: 16 (training), 64 (evaluation)
- **Learning Rate**: 2e-5
- **Optimizer**: AdamW
- **Weight Decay**: 0.01
- **Warmup Steps**: 500

### Training Process
- **Loss Function**: Cross-entropy loss for multi-class classification.
- **Evaluation Metric**: Accuracy on the validation set.
- **Training Duration**: 3 epochs, with logging every 10 steps.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results



### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1