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UIT-VSFC-Bert-CLSModel-v3
haihuynh/UIT-VSFC-Bert-CLSModel-v3
UIT-VSFC-Bert-CLSModel-v3 is a text classification model from haihuynh. 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
- Jun 20, 2024
- Updated
- Jun 21, 2024
- SHA
- be146584
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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. -->
UIT-VSFC-Bert-CLSModel-v3
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: 0.3998
- Accuracy: 0.8667
- F1: 0.5913
- Precision: 0.5783
- Recall: 0.6071
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-06
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 90 | 0.6253 | 0.7732 | 0.5277 | 0.5181 | 0.5425 |
| No log | 2.0 | 180 | 0.4288 | 0.8541 | 0.5827 | 0.5704 | 0.5985 |
| No log | 3.0 | 270 | 0.3998 | 0.8667 | 0.5913 | 0.5783 | 0.6071 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Jun 20, 2024
- Updated
- Jun 21, 2024