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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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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: distilbert-base-uncased-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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# distilbert-base-uncased-finetuned-ner |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2228 |
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- Precision: 0.8030 |
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- Recall: 0.8093 |
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- F1: 0.8061 |
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- Accuracy: 0.9545 |
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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: 0.0005 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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- lr_scheduler_warmup_ratio: 0.06 |
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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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| 0.3032 | 1.0 | 878 | 0.3241 | 0.6979 | 0.5912 | 0.6401 | 0.9168 | |
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| 0.2666 | 2.0 | 1756 | 0.2822 | 0.6475 | 0.6577 | 0.6525 | 0.9221 | |
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| 0.2025 | 3.0 | 2634 | 0.2402 | 0.7021 | 0.7273 | 0.7144 | 0.9369 | |
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| 0.1421 | 4.0 | 3512 | 0.2158 | 0.7283 | 0.7331 | 0.7307 | 0.9390 | |
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| 0.111 | 5.0 | 4390 | 0.2189 | 0.7442 | 0.7395 | 0.7418 | 0.9417 | |
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| 0.0813 | 6.0 | 5268 | 0.2196 | 0.7307 | 0.7812 | 0.7551 | 0.9442 | |
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| 0.0538 | 7.0 | 6146 | 0.2169 | 0.7594 | 0.8049 | 0.7815 | 0.9497 | |
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| 0.0389 | 8.0 | 7024 | 0.2133 | 0.7929 | 0.7991 | 0.7960 | 0.9520 | |
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| 0.0263 | 9.0 | 7902 | 0.2192 | 0.8002 | 0.7991 | 0.7996 | 0.9530 | |
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| 0.0141 | 10.0 | 8780 | 0.2224 | 0.8029 | 0.8097 | 0.8063 | 0.9546 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.0 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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