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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 for IPTC NewsCodes |
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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 a private dataset. |
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Built from a limited set of English, Swedish and Norwegian titles to classify news content within 16 categories as specified by the IPTC NewsCodes. |
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The model has been fine-tuned on a dataset that is greatly skewed, but has been slightly augmented to stabilize it. |
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## Model description |
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The model is intended to categorize Norwegian, Swedish and English news content within the specified 16 categories but is a test model for demonstration purposes. |
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It needs more data within several categories to provide 100% value but it will outperform Claude Haiku and GPT-3.5 on this use case. |
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## Intended uses & limitations |
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Use it to categorize news texts. Only set the category if the value is at least 60% for the label, otherwise the model is uncertain. |
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# Test examples |
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**Input:** Mann siktet for drapsforsøk på Slovakias statsministeren |
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**Output:** crime, law and justice |
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**Input:** Tre døde i kioskbrann i Tyskland |
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**Output:** disaster, accident, and emergency incident |
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**Input:** Kultfilm får Netflix-oppfølger. Kultfilmen «Happy Gilmore» fra 1996 får en oppfølger på Netflix. Det røper strømmetjenesten selv på X, tidligere Twitter. –Happy Gilmore er tilbake! |
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**Output:** arts, culture, entertainment and media |
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# Performance |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8030 |
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- Accuracy: 0.7431 |
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- F1: 0.7474 |
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- Precision: 0.7695 |
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- Recall: 0.7431 |
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See the performance (accuracy) for each label below: |
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- Arts, culture, entertainment and media: 0.6842 |
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- Conflict, war and peace: 0.7351 |
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- Crime, law and justice: 0.8918 |
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- Disaster, accident, and emergency incident: 0.8699 |
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- Economy, business, and finance: 0.6893 |
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- Environment: 0.4483 |
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- Health: 0.7222 |
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- Human interest: 0.3182 |
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- Labour: 0.5 |
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- Lifestyle and leisure: 0.5556 |
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- Politics: 0.7909 |
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- Science and technology: 0.4583 |
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- Society: 0.3538 |
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- Sport: 0.9615 |
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- Weather: 1.0 |
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- Religion: 0.0 |
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## Training and evaluation data |
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Trained with the trainer, setting a learning rate of 2e-05 and batch size of 16 for 3 epochs. |
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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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| 1.9761 | 0.2907 | 200 | 1.4046 | 0.6462 | 0.6164 | 0.6057 | 0.6462 | 0.3158 | 0.8315 | 0.7629 | 0.7055 | 0.5437 | 0.0 | 0.5 | 0.0 | 0.0 | 0.3333 | 0.4843 | 0.0 | 0.0833 | 0.0 | 0.9615 | 0.0 | |
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| 1.2153 | 0.5814 | 400 | 1.0225 | 0.6894 | 0.6868 | 0.7652 | 0.6894 | 0.7895 | 0.6554 | 0.8196 | 0.8562 | 0.6408 | 0.2414 | 0.8333 | 0.1364 | 0.0 | 0.6667 | 0.8467 | 0.0 | 0.375 | 0.0154 | 0.9615 | 1.0 | |
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| 0.954 | 0.8721 | 600 | 0.8858 | 0.7231 | 0.7138 | 0.7309 | 0.7231 | 0.7368 | 0.7795 | 0.8918 | 0.8699 | 0.6214 | 0.3448 | 0.8889 | 0.1818 | 1.0 | 0.5556 | 0.6899 | 0.0 | 0.25 | 0.0462 | 0.9615 | 1.0 | |
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| 0.6662 | 1.1628 | 800 | 0.9381 | 0.6881 | 0.7009 | 0.7618 | 0.6881 | 0.7895 | 0.6126 | 0.8454 | 0.8630 | 0.6505 | 0.4483 | 0.7222 | 0.2273 | 1.0 | 0.4444 | 0.8293 | 0.0 | 0.5417 | 0.2308 | 0.9615 | 1.0 | |
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| 0.5554 | 1.4535 | 1000 | 0.8791 | 0.7025 | 0.7124 | 0.7628 | 0.7025 | 0.7368 | 0.6478 | 0.9021 | 0.8562 | 0.6602 | 0.3103 | 0.7778 | 0.3636 | 0.5 | 0.5556 | 0.8084 | 0.0 | 0.5 | 0.1846 | 0.9615 | 1.0 | |
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| 0.4396 | 1.7442 | 1200 | 0.8275 | 0.7175 | 0.7280 | 0.7686 | 0.7175 | 0.7895 | 0.6631 | 0.8196 | 0.8836 | 0.6893 | 0.3793 | 0.8333 | 0.4091 | 0.5 | 0.5556 | 0.8362 | 0.0 | 0.4167 | 0.3692 | 0.9615 | 1.0 | |
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| 0.383 | 2.0349 | 1400 | 0.7929 | 0.745 | 0.7501 | 0.7653 | 0.745 | 0.6842 | 0.7841 | 0.8866 | 0.8767 | 0.7087 | 0.4483 | 0.7778 | 0.4091 | 0.5 | 0.5556 | 0.6899 | 0.0 | 0.4167 | 0.2923 | 0.9615 | 0.0 | |
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| 0.3418 | 2.3256 | 1600 | 0.8042 | 0.7438 | 0.7440 | 0.7686 | 0.7438 | 0.7895 | 0.7351 | 0.9072 | 0.8493 | 0.7864 | 0.4483 | 0.7778 | 0.3182 | 0.5 | 0.5556 | 0.7909 | 0.0 | 0.4167 | 0.1846 | 0.9615 | 0.0 | |
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| 0.248 | 2.6163 | 1800 | 0.8387 | 0.7275 | 0.7325 | 0.7610 | 0.7275 | 0.6842 | 0.6891 | 0.8814 | 0.8699 | 0.7573 | 0.4138 | 0.8333 | 0.4091 | 0.5 | 0.5556 | 0.8014 | 0.0 | 0.4167 | 0.2769 | 0.9615 | 0.0 | |
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| 0.2525 | 2.9070 | 2000 | 0.8137 | 0.735 | 0.7413 | 0.7697 | 0.735 | 0.6842 | 0.7106 | 0.8763 | 0.8699 | 0.6796 | 0.4483 | 0.7222 | 0.3636 | 0.5 | 0.5556 | 0.8153 | 0.0 | 0.4583 | 0.3385 | 0.9615 | 0.0 | |
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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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