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---
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
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