update model card README.md
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README.md
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---
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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: distilbert-base-uncased
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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: 0.9294722867573847
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- name: Recall
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type: recall
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value: 0.942611915180074
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- name: F1
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type: f1
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value: 0.9359959893048128
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- name: Accuracy
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type: accuracy
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value: 0.9859087804025609
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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
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This model was trained from scratch on the conll2003 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0559
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- Precision: 0.9295
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- Recall: 0.9426
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- F1: 0.9360
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- Accuracy: 0.9859
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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: 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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| 0.0824 | 1.0 | 1756 | 0.0594 | 0.9151 | 0.9265 | 0.9207 | 0.9837 |
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| 0.0376 | 2.0 | 3512 | 0.0538 | 0.9218 | 0.9387 | 0.9302 | 0.9854 |
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| 0.0212 | 3.0 | 5268 | 0.0559 | 0.9295 | 0.9426 | 0.9360 | 0.9859 |
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### Framework versions
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- Transformers 4.30.0
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- Pytorch 2.2.1+cu121
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- Datasets 2.19.1
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- Tokenizers 0.13.3
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