---
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
---

<!-- 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. -->

# bert-finetuned-sem_eval-english

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/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