roberta-finetuned-ner-vi
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0009
- Date: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39}
- Loc: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 124}
- Org: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 59}
- Per: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 70}
- Price: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 79}
- Product: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13}
- Overall Precision: 1.0
- Overall Recall: 1.0
- Overall F1: 1.0
- Overall Accuracy: 1.0
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: 8
- eval_batch_size: 8
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Date | Loc | Org | Per | Price | Product | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 100 | 0.0346 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 0.957983193277311, 'recall': 0.9193548387096774, 'f1': 0.9382716049382716, 'number': 124} | {'precision': 0.9622641509433962, 'recall': 0.864406779661017, 'f1': 0.9107142857142857, 'number': 59} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 70} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 79} | {'precision': 0.7647058823529411, 'recall': 1.0, 'f1': 0.8666666666666666, 'number': 13} | 0.9708 | 0.9531 | 0.9619 | 0.9919 |
| No log | 2.0 | 200 | 0.0060 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 0.968, 'recall': 0.9758064516129032, 'f1': 0.9718875502008033, 'number': 124} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 59} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 70} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 79} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | 0.9896 | 0.9922 | 0.9909 | 0.9979 |
| No log | 3.0 | 300 | 0.0013 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 124} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 59} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 70} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 79} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | 1.0 | 1.0 | 1.0 | 1.0 |
| No log | 4.0 | 400 | 0.0010 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 124} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 59} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 70} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 79} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0878 | 5.0 | 500 | 0.0009 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 39} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 124} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 59} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 70} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 79} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | 1.0 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for huy1211/roberta-finetuned-ner-vi
Base model
google-bert/bert-base-multilingual-cased