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bert-finetuned-sem_eval-english
balluk/bert-finetuned-sem_eval-english
bert-finetuned-sem_eval-english is a text classification model from balluk. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Oct 2, 2023
- Updated
- Oct 2, 2023
- SHA
- 8700cf88
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Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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 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
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Oct 2, 2023
- Updated
- Oct 2, 2023