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---
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license: apache-2.0
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base_model: distilroberta-base
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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: RoBERTa_conll_epoch_10
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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: 0.9443059019118869
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- name: Recall
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type: recall
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value: 0.9559071019858634
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- name: F1
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type: f1
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value: 0.9500710880655683
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- name: Accuracy
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type: accuracy
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value: 0.9882329477463103
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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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# RoBERTa_conll_epoch_10
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This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the conll2003 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0906
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- Precision: 0.9443
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- Recall: 0.9559
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- F1: 0.9501
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- Accuracy: 0.9882
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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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- 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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| 0.0839 | 1.0 | 1756 | 0.0705 | 0.9055 | 0.9303 | 0.9177 | 0.9827 |
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| 0.0454 | 2.0 | 3512 | 0.0690 | 0.9257 | 0.9431 | 0.9343 | 0.9853 |
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| 0.0272 | 3.0 | 5268 | 0.0590 | 0.9310 | 0.9495 | 0.9402 | 0.9865 |
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| 0.0183 | 4.0 | 7024 | 0.0803 | 0.9324 | 0.9515 | 0.9419 | 0.9862 |
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| 0.0129 | 5.0 | 8780 | 0.0747 | 0.9433 | 0.9517 | 0.9475 | 0.9872 |
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| 0.0079 | 6.0 | 10536 | 0.0792 | 0.9359 | 0.9534 | 0.9446 | 0.9874 |
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| 0.0055 | 7.0 | 12292 | 0.0785 | 0.9457 | 0.9549 | 0.9503 | 0.9879 |
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| 0.003 | 8.0 | 14048 | 0.0881 | 0.9438 | 0.9561 | 0.9499 | 0.9879 |
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| 0.001 | 9.0 | 15804 | 0.0875 | 0.9448 | 0.9562 | 0.9505 | 0.9879 |
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| 0.0008 | 10.0 | 17560 | 0.0906 | 0.9443 | 0.9559 | 0.9501 | 0.9882 |
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### Framework versions
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- Transformers 4.40.2
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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