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PhoBertLexical-finetuned_70KURL
gechim/PhoBertLexical-finetuned_70KURL
PhoBertLexical-finetuned_70KURL is a machine learning model from gechim. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Jun 3, 2024
- Updated
- Jun 4, 2024
- SHA
- 83f4db88
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. -->
PhoBertLexical-finetuned_70KURL
This model is a fine-tuned version of vinai/phobert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1508
- Accuracy: 0.9565
- F1: 0.9565
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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2150
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 0.4651 | 200 | 0.4128 | 0.8255 | 0.7984 |
| No log | 0.9302 | 400 | 0.2414 | 0.9038 | 0.9057 |
| No log | 1.3953 | 600 | 0.1894 | 0.9289 | 0.9294 |
| No log | 1.8605 | 800 | 0.1859 | 0.9293 | 0.9307 |
| 0.3236 | 2.3256 | 1000 | 0.1580 | 0.9423 | 0.9430 |
| 0.3236 | 2.7907 | 1200 | 0.1532 | 0.9405 | 0.9413 |
| 0.3236 | 3.2558 | 1400 | 0.1412 | 0.9478 | 0.9483 |
| 0.3236 | 3.7209 | 1600 | 0.1345 | 0.9514 | 0.9517 |
| 0.1539 | 4.1860 | 1800 | 0.1399 | 0.9498 | 0.9505 |
| 0.1539 | 4.6512 | 2000 | 0.1382 | 0.9504 | 0.9511 |
| 0.1539 | 5.1163 | 2200 | 0.1457 | 0.9492 | 0.9499 |
| 0.1539 | 5.5814 | 2400 | 0.1343 | 0.9543 | 0.9543 |
| 0.1146 | 6.0465 | 2600 | 0.1382 | 0.9573 | 0.9576 |
| 0.1146 | 6.5116 | 2800 | 0.1483 | 0.9518 | 0.9524 |
| 0.1146 | 6.9767 | 3000 | 0.1392 | 0.9501 | 0.9508 |
| 0.1146 | 7.4419 | 3200 | 0.1388 | 0.9500 | 0.9507 |
| 0.1146 | 7.9070 | 3400 | 0.1508 | 0.9565 | 0.9565 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
- Library
- transformers
- Type
- roberta
- Created
- Jun 3, 2024
- Updated
- Jun 4, 2024