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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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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: 0.8870349771350884 |
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name: Precision |
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- type: recall |
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value: 0.8575696021029992 |
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name: Recall |
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- type: f1 |
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value: 0.8720534629404617 |
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name: F1 |
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- type: accuracy |
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value: 0.9848236357672584 |
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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: 0.0815 |
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- Precision: 0.8870 |
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- Recall: 0.8576 |
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- F1: 0.8721 |
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- Accuracy: 0.9848 |
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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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| 0.0536 | 1.0 | 2904 | 0.0749 | 0.8749 | 0.8426 | 0.8585 | 0.9831 | |
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| 0.0269 | 2.0 | 5808 | 0.0754 | 0.8734 | 0.8471 | 0.8600 | 0.9840 | |
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| 0.0173 | 3.0 | 8712 | 0.0815 | 0.8870 | 0.8576 | 0.8721 | 0.9848 | |
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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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