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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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- 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: bert-large-uncased_ner_wikiann |
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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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args: en |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8383588049015558 |
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- name: Recall |
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type: recall |
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value: 0.8608794005372543 |
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- name: F1 |
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type: f1 |
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value: 0.8494698660714285 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9379407966623622 |
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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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# bert-large-uncased_ner_wikiann |
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This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the wikiann dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3373 |
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- Precision: 0.8384 |
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- Recall: 0.8609 |
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- F1: 0.8495 |
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- Accuracy: 0.9379 |
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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: cosine |
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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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| 0.3146 | 1.0 | 1250 | 0.2545 | 0.7956 | 0.8372 | 0.8159 | 0.9285 | |
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| 0.1973 | 2.0 | 2500 | 0.2438 | 0.8267 | 0.8546 | 0.8404 | 0.9349 | |
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| 0.1181 | 3.0 | 3750 | 0.2637 | 0.8320 | 0.8588 | 0.8452 | 0.9374 | |
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| 0.0647 | 4.0 | 5000 | 0.3175 | 0.8389 | 0.8627 | 0.8507 | 0.9387 | |
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| 0.0443 | 5.0 | 6250 | 0.3373 | 0.8384 | 0.8609 | 0.8495 | 0.9379 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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