---
library_name: transformers
license: mit
base_model: SamLowe/roberta-base-go_emotions
tags:
- generated_from_trainer
datasets:
- sem_eval_2018_task_1
metrics:
- f1
- accuracy
- precision
- recall
model-index:
- name: roberta-finetuned-sem_eval-english
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: sem_eval_2018_task_1
      type: sem_eval_2018_task_1
      config: subtask5.english
      split: validation
      args: subtask5.english
    metrics:
    - name: F1
      type: f1
      value: 0.7207163601161665
    - name: Accuracy
      type: accuracy
      value: 0.2799097065462754
    - name: Precision
      type: precision
      value: 0.7554540842212075
    - name: Recall
      type: recall
      value: 0.6890328551596483
---

<!-- 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. -->

# roberta-finetuned-sem_eval-english

This model is a fine-tuned version of [SamLowe/roberta-base-go_emotions](https://huggingface.co/SamLowe/roberta-base-go_emotions) on the sem_eval_2018_task_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3097
- F1: 0.7207
- Roc Auc: 0.8127
- Accuracy: 0.2799
- Precision: 0.7555
- Recall: 0.6890

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-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: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|:---------:|:------:|
| 0.3684        | 1.0   | 855  | 0.3003          | 0.7060 | 0.7973  | 0.3070   | 0.7749    | 0.6483 |
| 0.2776        | 2.0   | 1710 | 0.2930          | 0.7082 | 0.7978  | 0.3025   | 0.7823    | 0.6469 |
| 0.2441        | 3.0   | 2565 | 0.3019          | 0.7111 | 0.8025  | 0.2968   | 0.7684    | 0.6617 |
| 0.2205        | 4.0   | 3420 | 0.3008          | 0.7140 | 0.8060  | 0.2698   | 0.7618    | 0.6719 |
| 0.2002        | 5.0   | 4275 | 0.3058          | 0.7184 | 0.8109  | 0.2709   | 0.7555    | 0.6849 |
| 0.1844        | 6.0   | 5130 | 0.3097          | 0.7207 | 0.8127  | 0.2799   | 0.7555    | 0.6890 |
| 0.1692        | 7.0   | 5985 | 0.3110          | 0.7159 | 0.8102  | 0.2709   | 0.7482    | 0.6863 |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0