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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- anno_ctr |
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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: annoctr_bert_uncased |
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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: anno_ctr |
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type: anno_ctr |
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config: all_tags |
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split: test |
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args: all_tags |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7928388746803069 |
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- name: Recall |
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type: recall |
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value: 0.7809920945182869 |
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- name: F1 |
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type: f1 |
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value: 0.7868708971553611 |
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- name: Accuracy |
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type: accuracy |
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value: 0.936522196415268 |
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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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# annoctr_bert_uncased |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the anno_ctr dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3322 |
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- Precision: 0.7928 |
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- Recall: 0.7810 |
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- F1: 0.7869 |
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- Accuracy: 0.9365 |
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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: 1e-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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| 0.54 | 1.0 | 474 | 0.3452 | 0.6983 | 0.6601 | 0.6786 | 0.9137 | |
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| 0.3013 | 2.0 | 948 | 0.3466 | 0.7774 | 0.7018 | 0.7376 | 0.9240 | |
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| 0.0392 | 3.0 | 1422 | 0.3071 | 0.7851 | 0.7517 | 0.7680 | 0.9303 | |
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| 0.5695 | 4.0 | 1896 | 0.2941 | 0.7810 | 0.7617 | 0.7712 | 0.9334 | |
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| 0.0021 | 5.0 | 2370 | 0.3109 | 0.7928 | 0.7720 | 0.7823 | 0.9351 | |
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| 0.0419 | 6.0 | 2844 | 0.3020 | 0.7772 | 0.7796 | 0.7784 | 0.9341 | |
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| 0.2979 | 7.0 | 3318 | 0.3169 | 0.8019 | 0.7814 | 0.7915 | 0.9374 | |
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| 0.0017 | 8.0 | 3792 | 0.3260 | 0.7972 | 0.7778 | 0.7874 | 0.9365 | |
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| 0.0166 | 9.0 | 4266 | 0.3349 | 0.7935 | 0.7789 | 0.7861 | 0.9364 | |
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| 0.0685 | 10.0 | 4740 | 0.3322 | 0.7928 | 0.7810 | 0.7869 | 0.9365 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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