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roberta-large-ner-ghtk-cs-add-2label-16-new-data-3090-14Sep-1
Kudod/roberta-large-ner-ghtk-cs-add-2label-16-new-data-3090-14Sep-1
roberta-large-ner-ghtk-cs-add-2label-16-new-data-3090-14Sep-1 is a machine learning model from Kudod. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Model type
- xlm-roberta
- Access
- Public
- Created
- Sep 14, 2024
- Updated
- Sep 14, 2024
- SHA
- bf73a543
Model card
From the Hugging Face model README
<!-- 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. -->
roberta-large-ner-ghtk-cs-add-2label-16-new-data-3090-14Sep-1
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.8556
- Tk: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 116}
- A: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 431}
- Gày: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 34}
- Gày trừu tượng: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 488}
- Iờ: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38}
- Ã đơn: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 203}
- Đt: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 878}
- Đt trừu tượng: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 233}
- Overall Precision: 0.0
- Overall Recall: 0.0
- Overall F1: 0.0
- Overall Accuracy: 0.8099
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: 2.5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Tk | A | Gày | Gày trừu tượng | Iờ | Ã đơn | Đt | Đt trừu tượng | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.9188 | 1.0 | 2362 | 1.8497 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 116} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 431} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 34} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 488} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 203} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 878} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 233} | 0.0 | 0.0 | 0.0 | 0.8099 |
| 0.8676 | 2.0 | 4724 | 1.9285 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 116} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 431} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 34} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 488} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 203} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 878} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 233} | 0.0 | 0.0 | 0.0 | 0.8099 |
| 0.8347 | 3.0 | 7086 | 1.8423 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 116} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 431} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 34} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 488} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 203} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 878} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 233} | 0.0 | 0.0 | 0.0 | 0.8099 |
| 0.8612 | 4.0 | 9448 | 1.8190 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 116} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 431} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 34} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 488} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 203} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 878} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 233} | 0.0 | 0.0 | 0.0 | 0.8099 |
| 0.8821 | 5.0 | 11810 | 1.8033 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 116} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 431} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 34} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 488} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 203} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 878} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 233} | 0.0 | 0.0 | 0.0 | 0.8099 |
| 0.8634 | 6.0 | 14172 | 1.7820 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 116} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 431} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 34} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 488} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 203} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 878} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 233} | 0.0 | 0.0 | 0.0 | 0.8099 |
| 0.8386 | 7.0 | 16534 | 1.8550 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 116} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 431} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 34} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 488} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 203} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 878} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 233} | 0.0 | 0.0 | 0.0 | 0.8099 |
| 0.8657 | 8.0 | 18896 | 1.8338 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 116} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 431} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 34} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 488} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 203} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 878} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 233} | 0.0 | 0.0 | 0.0 | 0.8099 |
| 0.8921 | 9.0 | 21258 | 1.8489 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 116} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 431} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 34} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 488} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 203} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 878} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 233} | 0.0 | 0.0 | 0.0 | 0.8099 |
| 0.8639 | 10.0 | 23620 | 1.8556 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 116} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 431} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 34} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 488} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 38} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 203} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 878} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 233} | 0.0 | 0.0 | 0.0 | 0.8099 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Type
- xlm-roberta
- Created
- Sep 14, 2024
- Updated
- Sep 14, 2024