---
license: apache-2.0
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: train
      args: subtask5.english
    metrics:
    - name: F1
      type: f1
      value: 0.7113731269958242
    - name: Accuracy
      type: accuracy
      value: 0.28103837471783294
---

<!-- 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: 0.3131
- F1: 0.7114
- Roc Auc: 0.8046
- Accuracy: 0.2810

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4067        | 1.0   | 855  | 0.3205          | 0.6756 | 0.7766  | 0.2709   |
| 0.2828        | 2.0   | 1710 | 0.3062          | 0.7058 | 0.7973  | 0.3014   |
| 0.239         | 3.0   | 2565 | 0.3122          | 0.7100 | 0.8038  | 0.2810   |
| 0.2145        | 4.0   | 3420 | 0.3131          | 0.7114 | 0.8046  | 0.2810   |
| 0.1888        | 5.0   | 4275 | 0.3167          | 0.7096 | 0.8022  | 0.2844   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1