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--- |
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license: gpl-3.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mim_gold_ner |
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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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widget: |
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- text: Systurnar Guðrún og Monique átu einar á McDonalds og horfðu á Stöð 2, þar |
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glitti í Bruce Willis leika í Die Hard 2. |
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base_model: vesteinn/IceBERT |
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model-index: |
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- name: IceBERT-finetuned-ner |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: mim_gold_ner |
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type: mim_gold_ner |
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args: mim-gold-ner |
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metrics: |
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- type: precision |
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value: 0.9351994710160899 |
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name: Precision |
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- type: recall |
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value: 0.9440427188786294 |
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name: Recall |
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- type: f1 |
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value: 0.9396002878813043 |
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name: F1 |
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- type: accuracy |
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value: 0.9920330921021648 |
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name: Accuracy |
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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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# IceBERT-finetuned-ner |
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This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the mim_gold_ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0347 |
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- Precision: 0.9352 |
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- Recall: 0.9440 |
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- F1: 0.9396 |
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- Accuracy: 0.9920 |
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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: 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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| 0.0568 | 1.0 | 2929 | 0.0386 | 0.9114 | 0.9162 | 0.9138 | 0.9897 | |
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| 0.0325 | 2.0 | 5858 | 0.0325 | 0.9300 | 0.9363 | 0.9331 | 0.9912 | |
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| 0.0184 | 3.0 | 8787 | 0.0347 | 0.9352 | 0.9440 | 0.9396 | 0.9920 | |
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### Framework versions |
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- Transformers 4.11.0 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.12.1 |
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- Tokenizers 0.10.3 |
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