|
--- |
|
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 |
|
|
