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Training complete
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README.md
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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: 0.3723
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- Precision: 0.5534
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- Recall: 0.5362
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- F1: 0.5447
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- Accuracy: 0.9281
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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 | 0.4063 | 0.2711 | 0.2638 | 0.2674 | 0.8968 |
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| No log | 2.0 | 242 | 0.3501 | 0.4935 | 0.3220 | 0.3897 | 0.9196 |
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| No log | 3.0 | 363 | 0.2928 | 0.4839 | 0.4255 | 0.4528 | 0.9272 |
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| No log | 4.0 | 484 | 0.3419 | 0.5407 | 0.3957 | 0.4570 | 0.9247 |
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| 0.3258 | 5.0 | 605 | 0.3310 | 0.5431 | 0.4553 | 0.4954 | 0.9294 |
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| 0.3258 | 6.0 | 726 | 0.3424 | 0.5248 | 0.4809 | 0.5019 | 0.9274 |
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| 0.3258 | 7.0 | 847 | 0.3587 | 0.5471 | 0.5191 | 0.5328 | 0.9309 |
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| 0.3258 | 8.0 | 968 | 0.3639 | 0.5396 | 0.5220 | 0.5306 | 0.9281 |
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| 0.1033 | 9.0 | 1089 | 0.3695 | 0.5471 | 0.5277 | 0.5372 | 0.9276 |
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| 0.1033 | 10.0 | 1210 | 0.3723 | 0.5534 | 0.5362 | 0.5447 | 0.9281 |
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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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