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update model card README.md
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README.md
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---
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license: apache-2.0
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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-finetuned-ner-10epochs
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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-finetuned-ner-10epochs
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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: 0.0895
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- Precision: 0.9167
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- Recall: 0.9546
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- F1: 0.9352
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- Accuracy: 0.9888
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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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| 0.0061 | 1.0 | 2261 | 0.0961 | 0.8813 | 0.9436 | 0.9114 | 0.9869 |
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| 0.0241 | 2.0 | 4522 | 0.0566 | 0.9001 | 0.9502 | 0.9245 | 0.9878 |
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| 0.02 | 3.0 | 6783 | 0.0560 | 0.9010 | 0.9528 | 0.9261 | 0.9879 |
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| 0.0169 | 4.0 | 9044 | 0.0519 | 0.9045 | 0.9539 | 0.9285 | 0.9884 |
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| 0.0129 | 5.0 | 11305 | 0.0621 | 0.9073 | 0.9568 | 0.9314 | 0.9886 |
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| 0.009 | 6.0 | 13566 | 0.0623 | 0.9123 | 0.9451 | 0.9284 | 0.9883 |
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| 0.0078 | 7.0 | 15827 | 0.0727 | 0.9145 | 0.9473 | 0.9306 | 0.9886 |
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| 0.0056 | 8.0 | 18088 | 0.0806 | 0.9134 | 0.9535 | 0.9330 | 0.9882 |
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| 0.0034 | 9.0 | 20349 | 0.0856 | 0.9103 | 0.9546 | 0.9319 | 0.9886 |
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| 0.003 | 10.0 | 22610 | 0.0895 | 0.9167 | 0.9546 | 0.9352 | 0.9888 |
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### Framework versions
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- Transformers 4.30.1
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- Pytorch 2.0.1+cu117
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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