transformers tensorboard safetensors 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
library_name: transformers
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: 0.7071713147410359
      name: F1
    - type: accuracy
      value: 0.2866817155756208
      name: Accuracy
---

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# 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: 0.3063
- F1: 0.7072
- Roc Auc: 0.7999
- Accuracy: 0.2867

## 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: 16
- eval_batch_size: 16
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 428  | 0.3278          | 0.6765 | 0.7759  | 0.2641   |
| 0.3858        | 2.0   | 856  | 0.3032          | 0.6879 | 0.7804  | 0.2743   |
| 0.2836        | 3.0   | 1284 | 0.3017          | 0.7033 | 0.7957  | 0.2935   |
| 0.2446        | 4.0   | 1712 | 0.3060          | 0.7037 | 0.7970  | 0.2799   |
| 0.2225        | 5.0   | 2140 | 0.3063          | 0.7072 | 0.7999  | 0.2867   |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.1
