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--- |
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license: apache-2.0 |
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base_model: ethanyt/guwenbert-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- ched_ner |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: guwenbert-large-CHED-ner |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: ched_ner |
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type: ched_ner |
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config: ched_ner |
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split: validation |
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args: ched_ner |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7442799461641992 |
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- name: Recall |
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type: recall |
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value: 0.8069066147859922 |
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- name: F1 |
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type: f1 |
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value: 0.7743290548424737 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9666064635130461 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# guwenbert-large-CHED-ner |
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This model is a fine-tuned version of [ethanyt/guwenbert-large](https://huggingface.co/ethanyt/guwenbert-large) on the ched_ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1905 |
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- Precision: 0.7443 |
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- Recall: 0.8069 |
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- F1: 0.7743 |
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- Accuracy: 0.9666 |
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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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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 356 | 0.1420 | 0.6862 | 0.7573 | 0.72 | 0.9609 | |
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| 0.2304 | 2.0 | 712 | 0.1324 | 0.6907 | 0.7972 | 0.7401 | 0.9624 | |
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| 0.095 | 3.0 | 1068 | 0.1314 | 0.7268 | 0.7918 | 0.7579 | 0.9656 | |
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| 0.095 | 4.0 | 1424 | 0.1348 | 0.7248 | 0.7967 | 0.7590 | 0.9659 | |
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| 0.0613 | 5.0 | 1780 | 0.1525 | 0.7088 | 0.8147 | 0.7581 | 0.9635 | |
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| 0.0397 | 6.0 | 2136 | 0.1635 | 0.7224 | 0.8127 | 0.7649 | 0.9648 | |
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| 0.0397 | 7.0 | 2492 | 0.1693 | 0.7416 | 0.7986 | 0.7691 | 0.9662 | |
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| 0.0261 | 8.0 | 2848 | 0.1809 | 0.7338 | 0.8059 | 0.7682 | 0.9657 | |
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| 0.0164 | 9.0 | 3204 | 0.1904 | 0.7291 | 0.8127 | 0.7686 | 0.9655 | |
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| 0.0124 | 10.0 | 3560 | 0.1905 | 0.7443 | 0.8069 | 0.7743 | 0.9666 | |
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### Framework versions |
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- Transformers 4.43.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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