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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: bert-base-NER-finetuned-ner |
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results: [] |
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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-NER-finetuned-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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2391 |
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- Precision: 0.9245 |
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- Recall: 0.9186 |
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- F1: 0.9216 |
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- Accuracy: 0.9168 |
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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-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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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| No log | 0.37 | 100 | 0.5115 | 0.8204 | 0.8719 | 0.8454 | 0.8200 | |
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| No log | 0.75 | 200 | 0.3808 | 0.8684 | 0.8766 | 0.8725 | 0.8600 | |
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| No log | 1.12 | 300 | 0.3315 | 0.8900 | 0.8865 | 0.8882 | 0.8799 | |
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| No log | 1.49 | 400 | 0.3069 | 0.9036 | 0.8917 | 0.8976 | 0.8921 | |
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| 0.5306 | 1.87 | 500 | 0.2908 | 0.9066 | 0.8978 | 0.9022 | 0.8980 | |
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| 0.5306 | 2.24 | 600 | 0.2783 | 0.9114 | 0.9061 | 0.9087 | 0.9048 | |
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| 0.5306 | 2.61 | 700 | 0.2729 | 0.9151 | 0.9123 | 0.9137 | 0.9096 | |
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| 0.5306 | 2.99 | 800 | 0.2628 | 0.9157 | 0.9086 | 0.9121 | 0.9077 | |
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| 0.5306 | 3.36 | 900 | 0.2600 | 0.9207 | 0.9123 | 0.9165 | 0.9107 | |
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| 0.3037 | 3.73 | 1000 | 0.2539 | 0.9188 | 0.9134 | 0.9161 | 0.9110 | |
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| 0.3037 | 4.1 | 1100 | 0.2488 | 0.9229 | 0.9178 | 0.9203 | 0.9148 | |
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| 0.3037 | 4.48 | 1200 | 0.2449 | 0.9225 | 0.9170 | 0.9198 | 0.9146 | |
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| 0.3037 | 4.85 | 1300 | 0.2466 | 0.9230 | 0.9177 | 0.9203 | 0.9155 | |
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| 0.3037 | 5.22 | 1400 | 0.2415 | 0.9229 | 0.9188 | 0.9208 | 0.9161 | |
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| 0.2668 | 5.6 | 1500 | 0.2413 | 0.9237 | 0.9189 | 0.9213 | 0.9164 | |
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| 0.2668 | 5.97 | 1600 | 0.2391 | 0.9245 | 0.9186 | 0.9216 | 0.9168 | |
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| 0.2668 | 6.34 | 1700 | 0.2399 | 0.9245 | 0.9178 | 0.9211 | 0.9162 | |
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| 0.2668 | 6.72 | 1800 | 0.2369 | 0.9239 | 0.9181 | 0.9210 | 0.9164 | |
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| 0.2668 | 7.09 | 1900 | 0.2390 | 0.9239 | 0.9183 | 0.9211 | 0.9164 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.15.2 |
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