roberta-large-emopillars-contextless-goemotions

roberta-large-emopillars-contextless-goemotions

This model is a fine-tuned version of roberta-large-emopillars-contextless on the GoEmotions dataset. The base model was trained on EmoPillars' context-less subset.

<img src="https://huggingface.co/datasets/alex-shvets/images/resolve/main/emopillars_color_2.png" width="450">

Model description

The model is a multi-label classifier over 28 emotional classes for a context-less scenario. It detects emotions in the entire input (including context if provided).

How to use

Here is how to use this model:

>>> import torch
>>> from transformers import pipeline
>>> model_name = "roberta-large-emopillars-contextless-goemotions"
>>> threshold = 0.36
>>> emotions = [
>>>     "admiration", "amusement", "anger", "annoyance", "approval", "caring", "confusion",
>>>     "curiosity", "desire", "disappointment", "disapproval", "disgust", "embarrassment",
>>>     "excitement", "fear", "gratitude", "grief", "joy", "love", "nervousness", "optimism",
>>>     "pride", "realization", "relief", "remorse", "sadness", "surprise", "neutral"
>>> ]
>>> label_to_emotion = dict(zip(list(range(len(emotions))), emotions))
>>> device = torch.device("cuda" if torch.cuda.is_available() else "CPU")
>>> pipe = pipeline("text-classification", model=model_name, truncation=True,
>>>                 return_all_scores=True, device=-1 if device.type=="cpu" else 0)
>>> # input in a format f"{text}"
>>> utterances = [
>>>     "Ok is it just me or is anyone else getting goosebumps too???",
>>>     "Don’t know what to do",
>>>     "I’m glad you had a great time here! Wishing you safe travels!"
>>> ]
>>> outcome = pipe(utterances)
>>> dominant_classes = [
>>>     [prediction for prediction in example if prediction['score'] >= threshold]
>>>     for example in outcome
>>> ]
>>> for example in dominant_classes:
>>>     print(", ".join([
>>>         "%s: %.2lf" % (label_to_emotion[int(prediction['label'])], prediction['score']) 
>>>         for prediction in sorted(example, key=lambda x: x['score'], reverse=True)
>>>     ]))
curiosity: 0.88
confusion: 1.00
caring: 0.89, joy: 0.39

Training data

The training data consists of 43,410 samples of the GoEmotions dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 252
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Framework versions

• Transformers 4.45.0.dev0
• Pytorch 2.4.0a0+gite3b9b71
• Datasets 2.21.0
• Tokenizers 0.19.1

Evaluation

Scores for the evaluation on the GoEmotions test split:

For more details on the evaluation, please visit our GitHub repository or paper.

Citation information

If you use this model, please cite our paper:

@misc{shvets2025emopillarsknowledgedistillation,
      title={Emo Pillars: Knowledge Distillation to Support Fine-Grained Context-Aware and Context-Less Emotion Classification}, 
      author={Alexander Shvets},
      year={2025},
      eprint={2504.16856},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2504.16856}
}

Disclaimer

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The model published in this repository is intended for a generalist purpose and is available to third parties. This model may have bias and/or any other undesirable distortions.

When third parties deploy or provide systems and/or services to other parties using this model (or using systems based on this model) or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.

In no event shall the creator of the model be liable for any results arising from the use made by third parties of this model.

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