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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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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_column_bert-base-NER |
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results: [] |
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language: |
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- en |
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widget: |
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- >- |
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bestseller 620463000001 |
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other china lc waikiki mağazacilik hi̇zmetleri̇ ti̇c aş 630140000000 hilti |
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6204699090_BD 55L Toaster Oven with Double Glass |
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- >- |
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611020000001 italy Apparel other games 9W1964Z8 debenhams guangzhou hec |
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fashion leather co ltd |
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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_column_bert-base-NER |
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This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1872 |
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- Precision: 0.7623 |
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- Recall: 0.7753 |
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- F1: 0.7688 |
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- Accuracy: 0.9023 |
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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: 64 |
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- eval_batch_size: 64 |
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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 | 702 | 0.6427 | 0.3025 | 0.2180 | 0.2534 | 0.7415 | |
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| 0.9329 | 2.0 | 1404 | 0.4771 | 0.4343 | 0.3587 | 0.3929 | 0.7955 | |
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| 0.546 | 3.0 | 2106 | 0.3983 | 0.5157 | 0.4530 | 0.4823 | 0.8242 | |
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| 0.546 | 4.0 | 2808 | 0.3748 | 0.5089 | 0.4758 | 0.4918 | 0.8305 | |
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| 0.4339 | 5.0 | 3510 | 0.2947 | 0.6362 | 0.6146 | 0.6252 | 0.8656 | |
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| 0.3658 | 6.0 | 4212 | 0.2818 | 0.6421 | 0.6231 | 0.6325 | 0.8664 | |
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| 0.3658 | 7.0 | 4914 | 0.2459 | 0.7108 | 0.6983 | 0.7045 | 0.8834 | |
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| 0.3221 | 8.0 | 5616 | 0.2665 | 0.6586 | 0.6404 | 0.6494 | 0.8701 | |
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| 0.2914 | 9.0 | 6318 | 0.2449 | 0.6880 | 0.6768 | 0.6823 | 0.8793 | |
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| 0.2657 | 10.0 | 7020 | 0.2411 | 0.7014 | 0.6862 | 0.6937 | 0.8824 | |
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| 0.2657 | 11.0 | 7722 | 0.2179 | 0.7261 | 0.7228 | 0.7244 | 0.8902 | |
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| 0.2453 | 12.0 | 8424 | 0.2301 | 0.6922 | 0.6919 | 0.6920 | 0.8858 | |
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| 0.2295 | 13.0 | 9126 | 0.2352 | 0.6768 | 0.6836 | 0.6802 | 0.8832 | |
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| 0.2295 | 14.0 | 9828 | 0.2020 | 0.7545 | 0.7499 | 0.7522 | 0.8970 | |
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| 0.2155 | 15.0 | 10530 | 0.2012 | 0.7449 | 0.7508 | 0.7478 | 0.8974 | |
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| 0.2064 | 16.0 | 11232 | 0.2036 | 0.7282 | 0.7402 | 0.7341 | 0.8960 | |
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| 0.2064 | 17.0 | 11934 | 0.1976 | 0.7390 | 0.7496 | 0.7443 | 0.8974 | |
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| 0.1978 | 18.0 | 12636 | 0.1859 | 0.7688 | 0.7828 | 0.7757 | 0.9040 | |
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| 0.1895 | 19.0 | 13338 | 0.1917 | 0.7574 | 0.7691 | 0.7632 | 0.9014 | |
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| 0.186 | 20.0 | 14040 | 0.1872 | 0.7623 | 0.7753 | 0.7688 | 0.9023 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.13.2 |
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- Tokenizers 0.13.3 |
