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PhoBERT-train-aug_insert_w2v
ThuyNT03/PhoBERT-train-aug_insert_w2v
PhoBERT-train-aug_insert_w2v is a text classification model from ThuyNT03. 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
- roberta
- Access
- Public
- Created
- Aug 30, 2023
- Updated
- Aug 30, 2023
- SHA
- c5b59989
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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. -->
PhoBERT-train-aug_insert_w2v
This model is a fine-tuned version of vinai/phobert-base-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2218
- Accuracy: 0.71
- F1: 0.7185
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.861 | 1.0 | 85 | 0.7003 | 0.71 | 0.6826 |
| 0.532 | 2.0 | 170 | 0.6556 | 0.73 | 0.7305 |
| 0.361 | 3.0 | 255 | 0.7729 | 0.74 | 0.7346 |
| 0.2421 | 4.0 | 340 | 0.9271 | 0.65 | 0.6591 |
| 0.177 | 5.0 | 425 | 1.0259 | 0.71 | 0.7199 |
| 0.1242 | 6.0 | 510 | 1.2122 | 0.68 | 0.6905 |
| 0.093 | 7.0 | 595 | 1.2365 | 0.68 | 0.6889 |
| 0.0886 | 8.0 | 680 | 1.2218 | 0.71 | 0.7185 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Aug 30, 2023
- Updated
- Aug 30, 2023