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LTR_BERT_0_350_noTSD
xhorvat9/LTR_BERT_0_350_noTSD
LTR_BERT_0_350_noTSD is a text classification model from xhorvat9. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Apr 2, 2023
- Updated
- Apr 2, 2023
- SHA
- 3fcc8267
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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. -->
output
This model is a fine-tuned version of zhihan1996/DNA_bert_6 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3966
- Accuracy: 0.8492
- Precision: 0.8807
- Recall: 0.8283
- F1: 0.8537
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.4859 | 0.41 | 500 | 0.4180 | 0.8001 | 0.8552 | 0.7506 | 0.7995 |
| 0.3937 | 0.81 | 1000 | 0.4044 | 0.8217 | 0.8871 | 0.7612 | 0.8193 |
| 0.3426 | 1.22 | 1500 | 0.3740 | 0.8340 | 0.8765 | 0.8 | 0.8365 |
| 0.3068 | 1.63 | 2000 | 0.3839 | 0.8398 | 0.8808 | 0.8077 | 0.8426 |
| 0.2757 | 2.04 | 2500 | 0.4260 | 0.8386 | 0.8181 | 0.8950 | 0.8548 |
| 0.2211 | 2.44 | 3000 | 0.3966 | 0.8492 | 0.8807 | 0.8283 | 0.8537 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Apr 2, 2023
- Updated
- Apr 2, 2023