transformers pytorch bert text-classification generated_from_trainer dataset:sem_eval_2018_task_1 base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-uncased
datasets:
- sem_eval_2018_task_1
license: apache-2.0
metrics:
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-sem_eval-english
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: sem_eval_2018_task_1
      type: sem_eval_2018_task_1
      config: subtask5.english
      split: validation
      args: subtask5.english
    metrics:
    - type: f1
      value: 0.6611215454789374
      name: F1
    - type: accuracy
      value: 0.24717832957110608
      name: Accuracy
---

<!-- 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: 0.3263
- F1: 0.6611
- Roc Auc: 0.7630
- Accuracy: 0.2472

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.404         | 1.0   | 855  | 0.3263          | 0.6611 | 0.7630  | 0.2472   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
