---
library_name: transformers
base_model: michellejieli/emotion_text_classifier
tags:
- generated_from_trainer
metrics:
- f1
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 [michellejieli/emotion_text_classifier](https://huggingface.co/michellejieli/emotion_text_classifier) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2828
- F1: 0.7879
- Roc Auc: nan
- Hamming: 0.1039

## Model description

This model uses a lightweight RoBERTa checkpoint that has been fine-tuned on evaluating emotions to further be trained on recognizing climate disinformation.

## Intended uses & limitations

To be used as a submission for the Frugal AI competition

## Training and evaluation data

Dataset of text and labels available on Frugal AI competition page.

## Training procedure

Used a binarizer to tokenize the text and found a seemingly suitable model checkpoint as a good place to start! 

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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
- num_epochs: 4

### Training results



### Framework versions

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