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--- |
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license: apache-2.0 |
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base_model: distilbert-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: ner-classifier-distil-bert |
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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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# ner-classifier-distil-bert |
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0484 |
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- Precision: 0.9237 |
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- Recall: 0.9388 |
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- F1: 0.9312 |
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- Accuracy: 0.9929 |
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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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| 0.0319 | 1.0 | 3546 | 0.0326 | 0.8726 | 0.9023 | 0.8872 | 0.9882 | |
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| 0.0206 | 2.0 | 7092 | 0.0285 | 0.8967 | 0.9273 | 0.9117 | 0.9910 | |
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| 0.0133 | 3.0 | 10638 | 0.0295 | 0.9072 | 0.9307 | 0.9188 | 0.9916 | |
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| 0.0081 | 4.0 | 14184 | 0.0327 | 0.9127 | 0.9274 | 0.9200 | 0.9918 | |
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| 0.0038 | 5.0 | 17730 | 0.0346 | 0.9205 | 0.9297 | 0.9251 | 0.9922 | |
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| 0.0044 | 6.0 | 21276 | 0.0376 | 0.9258 | 0.9299 | 0.9278 | 0.9925 | |
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| 0.0017 | 7.0 | 24822 | 0.0427 | 0.9277 | 0.9256 | 0.9266 | 0.9923 | |
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| 0.0038 | 8.0 | 28368 | 0.0460 | 0.9170 | 0.9399 | 0.9283 | 0.9926 | |
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| 0.0008 | 9.0 | 31914 | 0.0473 | 0.9266 | 0.9344 | 0.9305 | 0.9928 | |
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| 0.0026 | 10.0 | 35460 | 0.0484 | 0.9237 | 0.9388 | 0.9312 | 0.9929 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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