metadata
license: cc-by-nc-4.0
datasets:
- stockmark/ner-wikipedia-dataset
language:
- ja
metrics:
- f1
- precision
- recall
tags:
- NER
- information extraction
- relation extraction
- summarization
- sentiment extraction
- question-answering
pipeline_tag: token-classification
library_name: gliner
vumichien/ner-jp-gliner
This model is a fine-tuned version of deberta-v3-base-japanese on the Japanese Ner Wikipedia dataset. It achieves the following results:
- Precision: 96.07%
- Recall: 89.16%
- F1 score: 92.49%
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:
- num_steps: 30000
- train_batch_size: 8
- eval_every: 3000
- warmup_ratio: 0.1
- scheduler_type: "cosine"
- loss_alpha: -1
- loss_gamma: 0
- label_smoothing: 0
- loss_reduction: "sum"
- lr_encoder: 1e-5
- lr_others: 5e-5
- weight_decay_encoder: 0.01
- weight_decay_other: 0.01
Training results
| Epoch | Training Loss |
|---|---|
| 1 | 1291.582200 |
| 2 | 53.290100 |
| 3 | 44.137400 |
| 4 | 35.286200 |
| 5 | 20.865500 |
| 6 | 15.890000 |
| 7 | 13.369600 |
| 8 | 11.599500 |
| 9 | 9.773400 |
| 10 | 8.372600 |
| 11 | 7.256200 |
| 12 | 6.521800 |
| 13 | 7.203800 |
| 14 | 7.032900 |
| 15 | 6.189700 |
| 16 | 6.897400 |
| 17 | 6.031700 |
| 18 | 5.329600 |
| 19 | 5.411300 |
| 20 | 5.253800 |
| 21 | 4.522000 |
| 22 | 5.107700 |
| 23 | 4.163300 |
| 24 | 4.185400 |
| 25 | 3.403100 |
| 26 | 3.272400 |
| 27 | 2.387800 |
| 28 | 3.039400 |
| 29 | 2.383000 |
| 30 | 1.895300 |
| 31 | 1.748700 |
| 32 | 1.864300 |
| 33 | 2.343000 |
| 34 | 1.356600 |
| 35 | 1.182000 |
| 36 | 0.894700 |
| 37 | 0.954900 |