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--- |
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license: mit |
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base_model: roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- fursov/gec_ner_val3 |
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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: ner-gec-roberta-v3 |
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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: fursov/gec_ner_val3 |
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type: fursov/gec_ner_val3 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.5705440070765149 |
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- name: Recall |
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type: recall |
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value: 0.43481191856545776 |
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- name: F1 |
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type: f1 |
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value: 0.493515436703776 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9566099116988466 |
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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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# ner-gec-roberta-v3 |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the fursov/gec_ner_val3 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1759 |
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- Precision: 0.5705 |
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- Recall: 0.4348 |
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- F1: 0.4935 |
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- Accuracy: 0.9566 |
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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: 5e-05 |
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- train_batch_size: 128 |
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- eval_batch_size: 8 |
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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: 10.0 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall | |
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|:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|:---------:|:------:| |
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| 0.2421 | 1.15 | 500 | 0.9349 | 0.0868 | 0.2389 | 0.1631 | 0.0591 | |
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| 0.2065 | 2.3 | 1000 | 0.9381 | 0.2139 | 0.2182 | 0.3006 | 0.1660 | |
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| 0.1729 | 3.46 | 1500 | 0.9446 | 0.3066 | 0.1986 | 0.4014 | 0.2480 | |
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| 0.1558 | 4.61 | 2000 | 0.9485 | 0.3556 | 0.1899 | 0.4544 | 0.2921 | |
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| 0.1546 | 5.76 | 2500 | 0.1857 | 0.4823 | 0.3191 | 0.3841 | 0.9504 | |
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| 0.1343 | 6.91 | 3000 | 0.1784 | 0.5302 | 0.3794 | 0.4423 | 0.9535 | |
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| 0.1163 | 8.06 | 3500 | 0.1767 | 0.5563 | 0.4094 | 0.4717 | 0.9556 | |
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| 0.1045 | 9.22 | 4000 | 0.1783 | 0.5595 | 0.4328 | 0.4880 | 0.9554 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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