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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- favsbot |
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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: bert-base-cased-NER-favsbot |
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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: favsbot |
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type: favsbot |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8461538461538461 |
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- name: Recall |
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type: recall |
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value: 0.88 |
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- name: F1 |
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type: f1 |
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value: 0.8627450980392156 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9444444444444444 |
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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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# bert-base-cased-NER-favsbot |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the favsbot dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1680 |
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- Precision: 0.8462 |
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- Recall: 0.88 |
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- F1: 0.8627 |
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- Accuracy: 0.9444 |
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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: 1.5e-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: 20 |
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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 | 7 | 1.8761 | 0.0 | 0.0 | 0.0 | 0.5833 | |
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| No log | 2.0 | 14 | 1.3530 | 0.0 | 0.0 | 0.0 | 0.5972 | |
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| No log | 3.0 | 21 | 1.0400 | 1.0 | 0.12 | 0.2143 | 0.6389 | |
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| No log | 4.0 | 28 | 0.7987 | 0.7895 | 0.6 | 0.6818 | 0.8194 | |
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| No log | 5.0 | 35 | 0.6055 | 0.85 | 0.68 | 0.7556 | 0.875 | |
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| No log | 6.0 | 42 | 0.4749 | 0.8696 | 0.8 | 0.8333 | 0.9167 | |
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| No log | 7.0 | 49 | 0.3838 | 0.84 | 0.84 | 0.8400 | 0.9444 | |
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| No log | 8.0 | 56 | 0.3084 | 0.88 | 0.88 | 0.88 | 0.9583 | |
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| No log | 9.0 | 63 | 0.2643 | 0.88 | 0.88 | 0.88 | 0.9583 | |
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| No log | 10.0 | 70 | 0.2360 | 0.8462 | 0.88 | 0.8627 | 0.9444 | |
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| No log | 11.0 | 77 | 0.2168 | 0.8462 | 0.88 | 0.8627 | 0.9444 | |
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| No log | 12.0 | 84 | 0.2031 | 0.8462 | 0.88 | 0.8627 | 0.9444 | |
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| No log | 13.0 | 91 | 0.1937 | 0.88 | 0.88 | 0.88 | 0.9583 | |
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| No log | 14.0 | 98 | 0.1853 | 0.8462 | 0.88 | 0.8627 | 0.9444 | |
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| No log | 15.0 | 105 | 0.1791 | 0.8462 | 0.88 | 0.8627 | 0.9444 | |
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| No log | 16.0 | 112 | 0.1757 | 0.8462 | 0.88 | 0.8627 | 0.9444 | |
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| No log | 17.0 | 119 | 0.1718 | 0.8462 | 0.88 | 0.8627 | 0.9444 | |
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| No log | 18.0 | 126 | 0.1698 | 0.8148 | 0.88 | 0.8462 | 0.9444 | |
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| No log | 19.0 | 133 | 0.1686 | 0.8148 | 0.88 | 0.8462 | 0.9444 | |
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| No log | 20.0 | 140 | 0.1680 | 0.8462 | 0.88 | 0.8627 | 0.9444 | |
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### Framework versions |
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- Transformers 4.21.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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