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--- |
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base_model: KB/bert-base-swedish-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: news_category_classification |
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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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# news_category_classification |
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This model is a fine-tuned version of [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8615 |
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- Accuracy: 0.7286 |
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- F1: 0.7300 |
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- Precision: 0.7351 |
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- Recall: 0.7286 |
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- Accuracy Label Arts, culture, entertainment and media: 0.8333 |
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- Accuracy Label Conflict, war and peace: 0.7234 |
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- Accuracy Label Crime, law and justice: 0.7919 |
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- Accuracy Label Disaster, accident, and emergency incident: 0.8931 |
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- Accuracy Label Economy, business, and finance: 0.7975 |
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- Accuracy Label Environment: 0.4375 |
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- Accuracy Label Health: 0.7 |
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- Accuracy Label Human interest: 0.3333 |
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- Accuracy Label Labour: 0.5 |
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- Accuracy Label Lifestyle and leisure: 0.5 |
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- Accuracy Label Politics: 0.6331 |
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- Accuracy Label Religion: 0.0 |
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- Accuracy Label Science and technology: 0.4167 |
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- Accuracy Label Society: 0.4561 |
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- Accuracy Label Sport: 0.9615 |
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- Accuracy Label Weather: 1.0 |
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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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Accuracy Label Arts, culture, entertainment and media | Accuracy Label Conflict, war and peace | Accuracy Label Crime, law and justice | Accuracy Label Disaster, accident, and emergency incident | Accuracy Label Economy, business, and finance | Accuracy Label Environment | Accuracy Label Health | Accuracy Label Human interest | Accuracy Label Labour | Accuracy Label Lifestyle and leisure | Accuracy Label Politics | Accuracy Label Religion | Accuracy Label Science and technology | Accuracy Label Society | Accuracy Label Sport | Accuracy Label Weather | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-----------------------------------------------------:|:--------------------------------------:|:-------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------:|:--------------------------:|:---------------------:|:-----------------------------:|:---------------------:|:------------------------------------:|:-----------------------:|:-----------------------:|:-------------------------------------:|:----------------------:|:--------------------:|:----------------------:| |
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| 1.7671 | 0.3373 | 200 | 1.5661 | 0.5554 | 0.5206 | 0.5828 | 0.5554 | 0.5833 | 0.7553 | 0.8960 | 0.3206 | 0.6709 | 0.125 | 0.7 | 0.0 | 0.5 | 0.5 | 0.2878 | 0.0 | 0.0 | 0.0351 | 0.9615 | 1.0 | |
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| 1.0248 | 0.6745 | 400 | 1.0774 | 0.6709 | 0.6591 | 0.6984 | 0.6709 | 0.9167 | 0.7979 | 0.8150 | 0.8626 | 0.7215 | 0.375 | 0.9 | 0.25 | 1.0 | 0.5 | 0.3094 | 0.0 | 0.4167 | 0.1930 | 0.9615 | 1.0 | |
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| 0.5845 | 1.0118 | 600 | 0.9907 | 0.6536 | 0.6563 | 0.6829 | 0.6536 | 0.9167 | 0.7287 | 0.6763 | 0.8779 | 0.7215 | 0.4375 | 0.8 | 0.0 | 1.0 | 0.75 | 0.3669 | 0.0 | 0.4167 | 0.4386 | 0.9231 | 1.0 | |
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| 0.6104 | 1.3491 | 800 | 0.8674 | 0.7240 | 0.7233 | 0.7333 | 0.7240 | 0.8333 | 0.7021 | 0.8324 | 0.8779 | 0.7848 | 0.5 | 0.7 | 0.25 | 1.0 | 0.75 | 0.6331 | 0.0 | 0.25 | 0.3684 | 0.9615 | 1.0 | |
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| 0.4223 | 1.6863 | 1000 | 0.8602 | 0.7240 | 0.7250 | 0.7387 | 0.7240 | 0.75 | 0.6755 | 0.8844 | 0.8550 | 0.7342 | 0.5 | 0.9 | 0.3333 | 1.0 | 0.625 | 0.6475 | 0.0 | 0.3333 | 0.3684 | 0.9615 | 0.0 | |
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| 0.3104 | 2.0236 | 1200 | 0.8565 | 0.7263 | 0.7266 | 0.7326 | 0.7263 | 0.8333 | 0.7181 | 0.8324 | 0.9084 | 0.7722 | 0.4375 | 0.7 | 0.25 | 0.5 | 0.75 | 0.5612 | 0.0 | 0.4167 | 0.4737 | 0.9615 | 1.0 | |
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| 0.2855 | 2.3609 | 1400 | 0.8981 | 0.7240 | 0.7283 | 0.7402 | 0.7240 | 0.75 | 0.7394 | 0.8324 | 0.8550 | 0.7975 | 0.5 | 0.7 | 0.3333 | 0.5 | 0.625 | 0.5899 | 0.0 | 0.4167 | 0.3860 | 0.9615 | 1.0 | |
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| 0.217 | 2.6981 | 1600 | 0.8667 | 0.7309 | 0.7292 | 0.7358 | 0.7309 | 0.75 | 0.7447 | 0.8382 | 0.8931 | 0.8481 | 0.375 | 0.8 | 0.3333 | 0.5 | 0.5 | 0.5396 | 0.0 | 0.4167 | 0.4561 | 0.9615 | 1.0 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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