armpln/finetuned_model_emotion_detection

<!-- 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. -->

finetunedmodelemotion_detection

This model is a fine-tuned version of jhu-clsp/mmBERT-base on the None dataset. It achieves the following results on the evaluation set:

• —Loss: 0.3387
• —F1 Macro: 0.5265

Model description

This model is a fine-tuned version of mmBERT for multi-label emotion classification in Spanish tweets.

The model was trained on the Spanish subset of the SemEval 2018 Task 1 Affect in Tweets dataset (Emotion Classification track). Given a tweet, the model predicts the presence or absence of eleven emotion categories:

anger anticipation disgust fear joy love optimism pessimism sadness surprise trust

The model is based on the multilingual transformer mmBERT and was fine-tuned using transfer learning with the Hugging Face Transformers library.

Intended uses & limitations

This model is intended for:

• —Emotion detection in Spanish social media text.
• —Sentiment and affect analysis experiments.

Limitations

• —The model was trained exclusively on Twitter data and may not generalize well to other domains.
• —Emotion labels are not exclusive; a tweet may express multiple emotions simultaneously.
• —Performance may decrease on texts containing slang, spelling mistakes, code-switching, or cultural references not represented in the training data.

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 5e-05
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 0.1
• —num_epochs: 3

Training results

Framework versions

• —Transformers 5.12.1
• —Pytorch 2.11.0+cu128
• —Datasets 5.0.0
• —Tokenizers 0.22.2