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update model card README.md
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README.md
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---
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tags:
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- generated_from_trainer
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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-finetuned-ner_swedish_test
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results: []
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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-finetuned-ner_swedish_test
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This model is a fine-tuned version of [KBLab/bert-base-swedish-cased-ner](https://huggingface.co/KBLab/bert-base-swedish-cased-ner) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0916
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- Precision: 0.6835
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- Recall: 0.6391
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- F1: 0.6606
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- Accuracy: 0.9788
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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: 8
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- eval_batch_size: 8
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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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| No log | 1.0 | 128 | 0.0980 | 0.6121 | 0.5976 | 0.6048 | 0.9749 |
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| No log | 2.0 | 256 | 0.0914 | 0.7255 | 0.6568 | 0.6894 | 0.9779 |
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| No log | 3.0 | 384 | 0.0916 | 0.6835 | 0.6391 | 0.6606 | 0.9788 |
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### Framework versions
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- Transformers 4.19.3
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- Pytorch 1.7.1
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- Datasets 2.2.2
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- Tokenizers 0.12.1
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