Instructions to use phucleDio/2_task_v2_sports_movies with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phucleDio/2_task_v2_sports_movies with Transformers:
# Load model directly from transformers import AutoTokenizer, PhoBERTMultiTask tokenizer = AutoTokenizer.from_pretrained("phucleDio/2_task_v2_sports_movies") model = PhoBERTMultiTask.from_pretrained("phucleDio/2_task_v2_sports_movies", device_map="auto") - Notebooks
- Google Colab
- Kaggle
2_task_v2_sports_movies
This model is a fine-tuned version of RonTon05/model_content_V2_test on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5897
- F1 Task1: 0.9978
- F1 Task2: 0.4244
- Acc Task1: 0.9985
- Acc Task2: 0.4533
- F1: 0.7111
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: 0.0003
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- 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: cosine
- lr_scheduler_warmup_steps: 348
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Task1 | F1 Task2 | Acc Task1 | Acc Task2 | F1 |
|---|---|---|---|---|---|---|---|---|
| 2.3783 | 1.0 | 116 | 2.1904 | 0.9978 | 0.1017 | 0.9985 | 0.2387 | 0.5498 |
| 2.1670 | 2.0 | 232 | 2.1006 | 0.9978 | 0.1741 | 0.9985 | 0.2828 | 0.5860 |
| 2.0689 | 3.0 | 348 | 1.9326 | 0.9978 | 0.2666 | 0.9985 | 0.3276 | 0.6322 |
| 1.9503 | 4.0 | 464 | 1.8881 | 0.9978 | 0.2554 | 0.9985 | 0.3406 | 0.6266 |
| 1.8918 | 5.0 | 580 | 1.8318 | 0.9978 | 0.3163 | 0.9985 | 0.3759 | 0.6571 |
| 1.8410 | 6.0 | 696 | 1.7996 | 0.9978 | 0.3435 | 0.9985 | 0.4149 | 0.6707 |
| 1.7993 | 7.0 | 812 | 1.6977 | 0.9978 | 0.3640 | 0.9985 | 0.4126 | 0.6809 |
| 1.7554 | 8.0 | 928 | 1.6767 | 0.9978 | 0.3900 | 0.9985 | 0.4230 | 0.6939 |
| 1.7368 | 9.0 | 1044 | 1.6585 | 0.9978 | 0.3834 | 0.9985 | 0.4157 | 0.6906 |
| 1.7082 | 10.0 | 1160 | 1.6302 | 0.9978 | 0.3846 | 0.9985 | 0.4111 | 0.6912 |
| 1.6917 | 11.0 | 1276 | 1.6157 | 0.9978 | 0.4201 | 0.9985 | 0.4510 | 0.7090 |
| 1.6658 | 12.0 | 1392 | 1.5944 | 0.9978 | 0.4225 | 0.9985 | 0.4571 | 0.7102 |
| 1.6686 | 13.0 | 1508 | 1.5929 | 0.9978 | 0.4225 | 0.9985 | 0.4548 | 0.7101 |
| 1.6499 | 14.0 | 1624 | 1.5921 | 0.9978 | 0.4195 | 0.9985 | 0.4502 | 0.7086 |
| 1.6509 | 15.0 | 1740 | 1.5897 | 0.9978 | 0.4244 | 0.9985 | 0.4533 | 0.7111 |
Framework versions
- Transformers 5.14.1
- Pytorch 2.11.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
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