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