Instructions to use RonTon05/New_MTL_Full_Finetuning_OverSampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RonTon05/New_MTL_Full_Finetuning_OverSampling with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, PhoBERTMultiTask tokenizer = AutoTokenizer.from_pretrained("RonTon05/New_MTL_Full_Finetuning_OverSampling") model = PhoBERTMultiTask.from_pretrained("RonTon05/New_MTL_Full_Finetuning_OverSampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,849 Bytes
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library_name: transformers
license: agpl-3.0
base_model: RonTon05/model_content_V2_test
tags:
- generated_from_trainer
model-index:
- name: New_MTL_Full_Finetuning_OverSampling
results: []
---
<!-- 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. -->
# New_MTL_Full_Finetuning_OverSampling
This model is a fine-tuned version of [RonTon05/model_content_V2_test](https://huggingface.co/RonTon05/model_content_V2_test) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4517
- F1 Task1: 0.9727
- F1 Task2: 0.7492
- Acc Task1: 0.9789
- Acc Task2: 0.9160
- F1 Macro: 0.8609
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 261
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Task1 | F1 Task2 | Acc Task1 | Acc Task2 | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:---------:|:--------:|
| 0.9477 | 1.0 | 261 | 0.6483 | 0.9686 | 0.3580 | 0.9757 | 0.8208 | 0.6633 |
| 0.5012 | 2.0 | 522 | 0.4314 | 0.9709 | 0.7120 | 0.9774 | 0.8852 | 0.8414 |
| 0.3632 | 3.0 | 783 | 0.3812 | 0.9722 | 0.7348 | 0.9786 | 0.9047 | 0.8535 |
| 0.2974 | 4.0 | 1044 | 0.3800 | 0.9742 | 0.7434 | 0.9800 | 0.9074 | 0.8588 |
| 0.2496 | 5.0 | 1305 | 0.3895 | 0.9728 | 0.7440 | 0.9790 | 0.9115 | 0.8584 |
| 0.2106 | 6.0 | 1566 | 0.4031 | 0.9731 | 0.7459 | 0.9791 | 0.9108 | 0.8595 |
| 0.1790 | 7.0 | 1827 | 0.4297 | 0.9734 | 0.7460 | 0.9794 | 0.9157 | 0.8597 |
| 0.1574 | 8.0 | 2088 | 0.4305 | 0.9731 | 0.7487 | 0.9792 | 0.9180 | 0.8609 |
| 0.1412 | 9.0 | 2349 | 0.4515 | 0.9743 | 0.7511 | 0.9801 | 0.9172 | 0.8627 |
| 0.1321 | 10.0 | 2610 | 0.4517 | 0.9727 | 0.7492 | 0.9789 | 0.9160 | 0.8609 |
### Framework versions
- Transformers 5.17.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.1
- Tokenizers 0.23.2
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