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BERT_FPB_finetuned
avinasht/BERT_FPB_finetuned
BERT_FPB_finetuned is a text classification model from avinasht. 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
- Jan 30, 2024
- Updated
- Jan 30, 2024
- SHA
- a2740659
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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. -->
BERT_FPB_finetuned
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6446
- Accuracy: 0.8650
- F1: 0.8648
- Precision: 0.8647
- Recall: 0.8650
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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.4364 | 1.0 | 246 | 0.4949 | 0.7872 | 0.7708 | 0.8266 | 0.7872 |
| 0.4302 | 2.0 | 492 | 0.4106 | 0.8398 | 0.8404 | 0.8414 | 0.8398 |
| 0.1547 | 3.0 | 738 | 0.4793 | 0.8558 | 0.8559 | 0.8561 | 0.8558 |
| 0.1471 | 4.0 | 984 | 0.6446 | 0.8650 | 0.8648 | 0.8647 | 0.8650 |
Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Jan 30, 2024
- Updated
- Jan 30, 2024