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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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datasets:
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- conll2003
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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: results
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results:
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- task:
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name: Token Classification
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type: token-classification
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dataset:
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name: conll2003
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type: conll2003
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config: conll2003
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split: validation
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args: conll2003
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metrics:
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- name: Precision
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type: precision
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value: 0.9307273626917367
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- name: Recall
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type: recall
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value: 0.9496802423426456
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- name: F1
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type: f1
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value: 0.9401082882132445
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- name: Accuracy
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type: accuracy
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value: 0.9863866486136458
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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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# results
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0635
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- Precision: 0.9307
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- Recall: 0.9497
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- F1: 0.9401
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- Accuracy: 0.9864
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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: 5e-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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- lr_scheduler_warmup_steps: 500
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- num_epochs: 3
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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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| 0.2313 | 0.2847 | 500 | 0.1403 | 0.8444 | 0.8696 | 0.8568 | 0.9626 |
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| 0.1088 | 0.5695 | 1000 | 0.0887 | 0.8717 | 0.9098 | 0.8903 | 0.9765 |
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| 0.1211 | 0.8542 | 1500 | 0.0846 | 0.9076 | 0.9238 | 0.9156 | 0.9784 |
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| 0.0503 | 1.1390 | 2000 | 0.0753 | 0.9101 | 0.9354 | 0.9226 | 0.9814 |
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| 0.0493 | 1.4237 | 2500 | 0.0630 | 0.9170 | 0.9421 | 0.9294 | 0.9833 |
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| 0.0624 | 1.7084 | 3000 | 0.0705 | 0.9277 | 0.9366 | 0.9321 | 0.9837 |
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| 0.0313 | 1.9932 | 3500 | 0.0675 | 0.9270 | 0.9426 | 0.9347 | 0.9843 |
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| 0.0335 | 2.2779 | 4000 | 0.0661 | 0.9284 | 0.9492 | 0.9387 | 0.9857 |
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| 0.0098 | 2.5626 | 4500 | 0.0693 | 0.9347 | 0.9473 | 0.9410 | 0.9849 |
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| 0.0099 | 2.8474 | 5000 | 0.0635 | 0.9307 | 0.9497 | 0.9401 | 0.9864 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.1+cu118
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- Datasets 2.19.2
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- Tokenizers 0.19.1
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