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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- wikiann |
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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: wiki_hu_ner |
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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: wikiann |
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type: wikiann |
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config: hu |
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split: validation |
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args: hu |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8669236159775753 |
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- name: Recall |
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type: recall |
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value: 0.8782479057219935 |
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- name: F1 |
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type: f1 |
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value: 0.872549019607843 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9632061446977205 |
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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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# wiki_hu_ner |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wikiann dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1585 |
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- Precision: 0.8669 |
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- Recall: 0.8782 |
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- F1: 0.8725 |
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- Accuracy: 0.9632 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 5 |
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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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| 0.2429 | 1.0 | 1250 | 0.1849 | 0.8047 | 0.8153 | 0.8100 | 0.9448 | |
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| 0.1371 | 2.0 | 2500 | 0.1505 | 0.8455 | 0.8577 | 0.8516 | 0.9576 | |
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| 0.0986 | 3.0 | 3750 | 0.1516 | 0.8520 | 0.8708 | 0.8613 | 0.9603 | |
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| 0.0695 | 4.0 | 5000 | 0.1500 | 0.8656 | 0.8745 | 0.8700 | 0.9624 | |
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| 0.0489 | 5.0 | 6250 | 0.1585 | 0.8669 | 0.8782 | 0.8725 | 0.9632 | |
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### Framework versions |
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- Transformers 4.32.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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