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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- few_nerd |
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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: Cybonto-distilbert-base-uncased-finetuned-ner-FewNerd |
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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: few_nerd |
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type: few_nerd |
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args: supervised |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7422259388187705 |
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- name: Recall |
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type: recall |
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value: 0.7830368683449253 |
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- name: F1 |
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type: f1 |
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value: 0.7620854216169805 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9386106950200795 |
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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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# Cybonto-distilbert-base-uncased-finetuned-ner-FewNerd |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the few_nerd dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2091 |
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- Precision: 0.7422 |
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- Recall: 0.7830 |
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- F1: 0.7621 |
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- Accuracy: 0.9386 |
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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: 32 |
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- eval_batch_size: 32 |
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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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| 0.1964 | 1.0 | 4118 | 0.1946 | 0.7302 | 0.7761 | 0.7525 | 0.9366 | |
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| 0.1685 | 2.0 | 8236 | 0.1907 | 0.7414 | 0.7776 | 0.7591 | 0.9384 | |
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| 0.145 | 3.0 | 12354 | 0.1967 | 0.7454 | 0.7816 | 0.7631 | 0.9388 | |
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| 0.1263 | 4.0 | 16472 | 0.2021 | 0.7402 | 0.7845 | 0.7617 | 0.9384 | |
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| 0.1114 | 5.0 | 20590 | 0.2091 | 0.7422 | 0.7830 | 0.7621 | 0.9386 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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