metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- sem_eval_2018_task_1
metrics:
- f1
- accuracy
model-index:
- name: bert-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: 0.7075236671649229
- name: Accuracy
type: accuracy
value: 0.28555304740406323
bert-finetuned-sem_eval-english
This model is a fine-tuned version of bert-base-uncased on the sem_eval_2018_task_1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3008
- F1: 0.7075
- Roc Auc: 0.8000
- Accuracy: 0.2856
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.3964 | 1.0 | 855 | 0.3197 | 0.6852 | 0.7849 | 0.2810 |
| 0.2788 | 2.0 | 1710 | 0.3039 | 0.7049 | 0.7978 | 0.2912 |
| 0.2347 | 3.0 | 2565 | 0.3008 | 0.7075 | 0.8000 | 0.2856 |
| 0.2094 | 4.0 | 3420 | 0.3091 | 0.7041 | 0.7976 | 0.2856 |
| 0.1886 | 5.0 | 4275 | 0.3122 | 0.7068 | 0.8011 | 0.2810 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3