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---
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license: apache-2.0
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base_model: bert-base-uncased
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tags:
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- generated_from_trainer
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datasets:
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- sem_eval_2018_task_1
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metrics:
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- f1
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- accuracy
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model-index:
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- name: bert-finetuned-sem_eval-english
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: sem_eval_2018_task_1
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type: sem_eval_2018_task_1
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config: subtask5.english
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split: validation
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args: subtask5.english
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metrics:
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- name: F1
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type: f1
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value: 0.6643726839597671
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- name: Accuracy
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type: accuracy
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value: 0.2562076749435666
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# bert-finetuned-sem_eval-english
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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.
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It achieves the following results on the evaluation set:
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- Loss: 0.3269
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- F1: 0.6644
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- Roc Auc: 0.7665
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- Accuracy: 0.2562
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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| 0.4045 | 1.0 | 855 | 0.3269 | 0.6644 | 0.7665 | 0.2562 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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