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bert-finetuned-sem_eval-english
NainaSuraj/bert-finetuned-sem_eval-english
bert-finetuned-sem_eval-english is a text classification model from NainaSuraj. 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
- Mar 28, 2024
- Updated
- Mar 28, 2024
- SHA
- f3eb50c9
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Base models
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. -->
Notes tagger
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3223
- F1: 0.8380
- Roc Auc: 0.8801
- Accuracy: 0.5157
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: 0.0001
- 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 |
|---|---|---|---|---|---|---|
| 0.3549 | 1.0 | 584 | 0.3053 | 0.7922 | 0.8376 | 0.4682 |
| 0.2657 | 2.0 | 1168 | 0.2684 | 0.8279 | 0.8678 | 0.5087 |
| 0.2232 | 3.0 | 1752 | 0.2917 | 0.8330 | 0.8740 | 0.5093 |
| 0.1695 | 4.0 | 2336 | 0.2991 | 0.8349 | 0.8774 | 0.5145 |
| 0.1215 | 5.0 | 2920 | 0.3223 | 0.8380 | 0.8801 | 0.5157 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Mar 28, 2024
- Updated
- Mar 28, 2024