Distilbert Base Uncased Finetuned On Emotions Data

🚀 distilbert-base-uncased_finetuned_on_emotions_data

This model is a fine - tuned version of DistilBERT, designed to classify text into different emotional categories.

🚀 Quick Start

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: 0.1561
• Accuracy: 0.933
• F1: 0.9328

✨ Features

This model is designed to analyze text and classify it into different emotional categories, such as joy, sadness, anger, etc. It has been trained on a dataset specifically labeled with emotions, allowing it to identify the emotional tone of the input text. The model works by processing the text and predicting which emotion best fits the given context.

📚 Documentation

Intended uses & limitations

More information needed.

Limitations

• The model may confuse "fear" and "anger" because both emotions can be expressed in similar ways, especially in situations involving frustration, stress, or danger. Additionally, the language used to express these emotions might overlap, such as words like "nervous," "frustrated," or "threatened," which can be interpreted as either fear or anger depending on the context. This overlap in linguistic cues can make it challenging for the model to distinguish between the two emotions. Similarly, it may also have confusion between joy and love.


Training and evaluation data

I've used emotion data available on huggingface. Training data: emotion['train'] evaluation data: emotion['evaluation']

Confusion matrix


🔧 Technical Details

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: Use adamw_torch with betas=(0.9,0.999) and epsilon = 1e - 08 and optimizer_args = No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 4

Training results

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0

📄 License

This model is released under the Apache 2.0 license.

📦 Information Table