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--- |
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pipeline_tag: text-classification |
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library_name: transformers |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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datasets: |
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- dair-ai/emotion |
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language: |
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- en |
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model-index: |
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- name: results |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# results |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1699 |
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- Accuracy: 0.941 |
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## Model Description |
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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. |
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- **Model Type**: Text Classification |
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- **Base Model**: [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased) |
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- **Fine-Tuning Task**: Emotion Recognition (6 classes) |
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- **Languages**: English |
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- **License**: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
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## Intended Uses & Limitations |
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### Intended Uses |
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- **Emotion Classification**: Classify text into one of six emotions: sadness, joy, love, anger, fear, or surprise. |
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- **Sentiment Analysis**: Infer sentiment (e.g., joy as positive, anger as negative) based on predicted emotions, though not explicitly trained for this purpose. |
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- **Chatbots and Virtual Assistants**: Enhance conversational AI by detecting user emotions for empathetic responses. |
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- **Content Moderation**: Identify content with strong emotions like anger or fear for moderation purposes. |
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### Limitations |
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- **Emotion Granularity**: Restricted to six emotions, potentially missing nuanced or complex emotional states. |
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- **Contextual Understanding**: May struggle with sarcasm, irony, or emotions requiring deeper context. |
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- **Language**: Trained on English text only, with limited performance on other languages. |
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- **Dataset Bias**: Performance may reflect biases in the training data, such as underrepresentation of certain emotional expressions. |
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- **Short Texts**: Suboptimal performance on very short inputs (e.g., single words) due to limited context. |
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## Training and Evaluation Data |
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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: |
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- **0**: sadness |
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- **1**: joy |
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- **2**: love |
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- **3**: anger |
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- **4**: fear |
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- **5**: surprise |
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The dataset is divided as follows: |
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- **Training Set**: 16,000 samples |
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- **Validation Set**: 2,000 samples |
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- **Test Set**: 2,000 samples |
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The dataset is balanced across the six emotion classes, promoting effective learning for each category. |
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## Training Procedure |
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### Preprocessing |
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- **Tokenization**: Text was tokenized using the DistilBERT tokenizer, with a maximum sequence length of 512 tokens. Padding and truncation ensured uniform input sizes. |
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- **Data Formatting**: Converted to PyTorch tensors for training compatibility. |
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## Demo |
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Try the model in action [here](https://huggingface.co/spaces/YonasMersha/emotion-classifier). |
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### Training Hyperparameters |
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Fine-tuning was conducted using the Hugging Face `Trainer` API with: |
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- **Epochs**: 3 |
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- **Batch Size**: 16 (training), 64 (evaluation) |
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- **Learning Rate**: 2e-5 |
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- **Optimizer**: AdamW |
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- **Weight Decay**: 0.01 |
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- **Warmup Steps**: 500 |
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### Training Process |
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- **Loss Function**: Cross-entropy loss for multi-class classification. |
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- **Evaluation Metric**: Accuracy on the validation set. |
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- **Training Duration**: 3 epochs, with logging every 10 steps. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 3 |
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### Training results |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.1 |
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- Tokenizers 0.21.1 |
