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bert-large-cased-maplestory-ner
nxaliao/bert-large-cased-maplestory-ner
bert-large-cased-maplestory-ner is a token classification model from nxaliao. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Apr 3, 2024
- Updated
- Apr 3, 2024
- SHA
- 94fcdc66
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Base models
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. -->
bert-large-cased-maplestory-ner
This model is a fine-tuned version of bert-large-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1530
- Precision: 0.7916
- Recall: 0.8156
- F1: 0.8034
- Accuracy: 0.9642
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.208 | 1.0 | 2712 | 0.1728 | 0.7392 | 0.7582 | 0.7486 | 0.9525 |
| 0.1204 | 2.0 | 5424 | 0.1476 | 0.7951 | 0.7949 | 0.7950 | 0.9626 |
| 0.0682 | 3.0 | 8136 | 0.1530 | 0.7916 | 0.8156 | 0.8034 | 0.9642 |
Framework versions
- Transformers 4.39.3
- Pytorch 1.12.0
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Apr 3, 2024
- Updated
- Apr 3, 2024