Instructions to use MrPickle191/phobert-ner-vietnamese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MrPickle191/phobert-ner-vietnamese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="MrPickle191/phobert-ner-vietnamese")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("MrPickle191/phobert-ner-vietnamese") model = AutoModelForTokenClassification.from_pretrained("MrPickle191/phobert-ner-vietnamese", device_map="auto") - Notebooks
- Google Colab
- Kaggle
phobert-ner-vietnamese
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: 0.1283
- F1: 0.9248
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: 8
- seed: 42
- 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
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 315 | 0.2700 | 0.8630 |
| 0.4608 | 2.0 | 630 | 0.1919 | 0.9024 |
| 0.4608 | 3.0 | 945 | 0.1447 | 0.9147 |
| 0.1231 | 4.0 | 1260 | 0.1323 | 0.9227 |
| 0.0773 | 5.0 | 1575 | 0.1283 | 0.9248 |
Framework versions
- Transformers 5.20.2
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.23.3
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Model tree for MrPickle191/phobert-ner-vietnamese
Base model
vinai/phobert-base-v2