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--- |
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license: mit |
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base_model: dslim/bert-base-NER |
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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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- india 0S0308Z8 trudeau 3000 Ravensburger Hamnoy, Lofoten of gold bestseller 620463000001 |
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- other china lc waikiki mağazacilik hi̇zmetleri̇ ti̇c aş 630140000000 hilti 6204699090_BD 55L Toaster Oven with Double Glass |
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- 611020000001 italy Apparel other games 9W1964Z8 debenhams guangzhou hec 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: 0.1855 |
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- Precision: 0.7651 |
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- Recall: 0.7786 |
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- F1: 0.7718 |
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- Accuracy: 0.9026 |
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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 | 0.7382 | 0.2576 | 0.1887 | 0.2178 | 0.7127 | |
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| 0.9356 | 2.0 | 1404 | 0.4405 | 0.5139 | 0.4331 | 0.4700 | 0.8157 | |
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| 0.5445 | 3.0 | 2106 | 0.3608 | 0.5712 | 0.5143 | 0.5413 | 0.8404 | |
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| 0.5445 | 4.0 | 2808 | 0.3226 | 0.6188 | 0.5840 | 0.6009 | 0.8550 | |
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| 0.4316 | 5.0 | 3510 | 0.2757 | 0.6788 | 0.6569 | 0.6676 | 0.8728 | |
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| 0.3605 | 6.0 | 4212 | 0.2828 | 0.6584 | 0.6346 | 0.6463 | 0.8697 | |
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| 0.3605 | 7.0 | 4914 | 0.2456 | 0.7108 | 0.6926 | 0.7015 | 0.8820 | |
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| 0.3153 | 8.0 | 5616 | 0.2385 | 0.7055 | 0.6986 | 0.7021 | 0.8855 | |
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| 0.282 | 9.0 | 6318 | 0.2345 | 0.7044 | 0.6961 | 0.7002 | 0.8853 | |
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| 0.2587 | 10.0 | 7020 | 0.2313 | 0.7081 | 0.7049 | 0.7065 | 0.8862 | |
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| 0.2587 | 11.0 | 7722 | 0.2026 | 0.7734 | 0.7537 | 0.7634 | 0.8968 | |
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| 0.239 | 12.0 | 8424 | 0.1980 | 0.7651 | 0.7687 | 0.7669 | 0.8991 | |
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| 0.2241 | 13.0 | 9126 | 0.2091 | 0.7368 | 0.7423 | 0.7395 | 0.8936 | |
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| 0.2241 | 14.0 | 9828 | 0.1954 | 0.7693 | 0.7684 | 0.7689 | 0.8987 | |
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| 0.2124 | 15.0 | 10530 | 0.1916 | 0.7668 | 0.7749 | 0.7708 | 0.9008 | |
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| 0.2025 | 16.0 | 11232 | 0.1841 | 0.7699 | 0.7794 | 0.7746 | 0.9024 | |
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| 0.2025 | 17.0 | 11934 | 0.1938 | 0.7527 | 0.7626 | 0.7576 | 0.8992 | |
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| 0.193 | 18.0 | 12636 | 0.1849 | 0.7705 | 0.7841 | 0.7772 | 0.9040 | |
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| 0.1877 | 19.0 | 13338 | 0.1927 | 0.7510 | 0.7649 | 0.7579 | 0.9005 | |
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| 0.1821 | 20.0 | 14040 | 0.1855 | 0.7651 | 0.7786 | 0.7718 | 0.9026 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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