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--- |
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license: apache-2.0 |
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base_model: bert-base-cased |
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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-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-finetuned-ner |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co./bert-base-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3788 |
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- Precision: 0.5395 |
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- Recall: 0.5234 |
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- F1: 0.5313 |
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- Accuracy: 0.9307 |
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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: 8 |
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- eval_batch_size: 8 |
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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 | 1.0 | 121 | 0.4099 | 0.2393 | 0.2383 | 0.2388 | 0.8962 | |
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| No log | 2.0 | 242 | 0.3394 | 0.4340 | 0.3220 | 0.3697 | 0.9180 | |
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| No log | 3.0 | 363 | 0.2952 | 0.5017 | 0.4170 | 0.4555 | 0.9271 | |
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| No log | 4.0 | 484 | 0.3419 | 0.5301 | 0.4 | 0.4559 | 0.9284 | |
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| 0.321 | 5.0 | 605 | 0.3269 | 0.5354 | 0.4723 | 0.5019 | 0.9313 | |
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| 0.321 | 6.0 | 726 | 0.3382 | 0.5091 | 0.4780 | 0.4931 | 0.9285 | |
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| 0.321 | 7.0 | 847 | 0.3528 | 0.5489 | 0.5177 | 0.5328 | 0.9315 | |
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| 0.321 | 8.0 | 968 | 0.3623 | 0.5446 | 0.5191 | 0.5316 | 0.9306 | |
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| 0.0997 | 9.0 | 1089 | 0.3706 | 0.5225 | 0.5262 | 0.5244 | 0.9283 | |
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| 0.0997 | 10.0 | 1210 | 0.3788 | 0.5395 | 0.5234 | 0.5313 | 0.9307 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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