Text Classification
Transformers
Safetensors
xlm-roberta
text2text-classification
Generated from Trainer
text-embeddings-inference
Instructions to use TungCan/tuning-sentiment-5cdviso-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TungCan/tuning-sentiment-5cdviso-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TungCan/tuning-sentiment-5cdviso-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TungCan/tuning-sentiment-5cdviso-v2") model = AutoModelForSequenceClassification.from_pretrained("TungCan/tuning-sentiment-5cdviso-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
tuning-sentiment-5cdviso-v2
This model is a fine-tuned version of 5CD-AI/Vietnamese-Sentiment-visobert on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5569
- Accuracy: 0.9076
- F1: 0.9077
- Precision: 0.9080
- Recall: 0.9076
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.2969 | 1.0776 | 500 | 0.3242 | 0.8963 | 0.8969 | 0.8984 | 0.8963 |
| 0.1866 | 2.1552 | 1000 | 0.5569 | 0.9076 | 0.9077 | 0.9080 | 0.9076 |
| 0.2202 | 3.2328 | 1500 | 1.0544 | 0.9067 | 0.9069 | 0.9080 | 0.9067 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
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Model tree for TungCan/tuning-sentiment-5cdviso-v2
Base model
5CD-AI/Vietnamese-Sentiment-visobert