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--- |
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library_name: transformers |
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base_model: Fsoft-AIC/videberta-base |
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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: videberta-base-ner-ghtk-hirach_NER-first_1000_data-3090-15Nov |
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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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# videberta-base-ner-ghtk-hirach_NER-first_1000_data-3090-15Nov |
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This model is a fine-tuned version of [Fsoft-AIC/videberta-base](https://huggingface.co/Fsoft-AIC/videberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0921 |
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- Accuracy: 0.9816 |
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- F1: 0.0426 |
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- Precision: 0.25 |
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- Recall: 0.0233 |
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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: 2.5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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: 40 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 250 | 0.0910 | 0.9825 | 0.0 | 0.0 | 0.0 | |
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| 0.1567 | 2.0 | 500 | 0.0949 | 0.9825 | 0.0 | 0.0 | 0.0 | |
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| 0.1567 | 3.0 | 750 | 0.0959 | 0.9825 | 0.0 | 0.0 | 0.0 | |
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| 0.0772 | 4.0 | 1000 | 0.0962 | 0.9825 | 0.0 | 0.0 | 0.0 | |
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| 0.0772 | 5.0 | 1250 | 0.0975 | 0.9825 | 0.0 | 0.0 | 0.0 | |
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| 0.0767 | 6.0 | 1500 | 0.0969 | 0.9825 | 0.0 | 0.0 | 0.0 | |
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| 0.0767 | 7.0 | 1750 | 0.0984 | 0.9825 | 0.0 | 0.0 | 0.0 | |
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| 0.0758 | 8.0 | 2000 | 0.0966 | 0.9825 | 0.0 | 0.0 | 0.0 | |
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| 0.0758 | 9.0 | 2250 | 0.0960 | 0.9825 | 0.0 | 0.0 | 0.0 | |
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| 0.0739 | 10.0 | 2500 | 0.0955 | 0.9825 | 0.0 | 0.0 | 0.0 | |
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| 0.0739 | 11.0 | 2750 | 0.0958 | 0.9825 | 0.0 | 0.0 | 0.0 | |
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| 0.0711 | 12.0 | 3000 | 0.0940 | 0.9825 | 0.0 | 0.0 | 0.0 | |
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| 0.0711 | 13.0 | 3250 | 0.0942 | 0.9820 | 0.0 | 0.0 | 0.0 | |
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| 0.0672 | 14.0 | 3500 | 0.0958 | 0.9825 | 0.0 | 0.0 | 0.0 | |
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| 0.0672 | 15.0 | 3750 | 0.0943 | 0.9825 | 0.0851 | 0.5 | 0.0465 | |
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| 0.0639 | 16.0 | 4000 | 0.0926 | 0.9829 | 0.0455 | 1.0 | 0.0233 | |
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| 0.0639 | 17.0 | 4250 | 0.0964 | 0.9820 | 0.0435 | 0.3333 | 0.0233 | |
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| 0.0611 | 18.0 | 4500 | 0.0970 | 0.9820 | 0.0435 | 0.3333 | 0.0233 | |
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| 0.0611 | 19.0 | 4750 | 0.0969 | 0.9825 | 0.0444 | 0.5 | 0.0233 | |
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| 0.058 | 20.0 | 5000 | 0.0952 | 0.9820 | 0.0435 | 0.3333 | 0.0233 | |
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| 0.058 | 21.0 | 5250 | 0.0950 | 0.9820 | 0.0435 | 0.3333 | 0.0233 | |
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| 0.0547 | 22.0 | 5500 | 0.0954 | 0.9816 | 0.0426 | 0.25 | 0.0233 | |
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| 0.0547 | 23.0 | 5750 | 0.0963 | 0.9816 | 0.0426 | 0.25 | 0.0233 | |
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| 0.0525 | 24.0 | 6000 | 0.0946 | 0.9820 | 0.0435 | 0.3333 | 0.0233 | |
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| 0.0525 | 25.0 | 6250 | 0.0942 | 0.9820 | 0.0435 | 0.3333 | 0.0233 | |
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| 0.0502 | 26.0 | 6500 | 0.0909 | 0.9825 | 0.0444 | 0.5 | 0.0233 | |
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| 0.0502 | 27.0 | 6750 | 0.0958 | 0.9816 | 0.0426 | 0.25 | 0.0233 | |
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| 0.048 | 28.0 | 7000 | 0.0934 | 0.9808 | 0.0408 | 0.1667 | 0.0233 | |
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| 0.048 | 29.0 | 7250 | 0.0946 | 0.9804 | 0.04 | 0.1429 | 0.0233 | |
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| 0.0458 | 30.0 | 7500 | 0.0938 | 0.9808 | 0.0408 | 0.1667 | 0.0233 | |
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| 0.0458 | 31.0 | 7750 | 0.0913 | 0.9829 | 0.0870 | 0.6667 | 0.0465 | |
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| 0.044 | 32.0 | 8000 | 0.0913 | 0.9816 | 0.0426 | 0.25 | 0.0233 | |
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| 0.044 | 33.0 | 8250 | 0.0915 | 0.9808 | 0.0408 | 0.1667 | 0.0233 | |
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| 0.0427 | 34.0 | 8500 | 0.0924 | 0.9808 | 0.0408 | 0.1667 | 0.0233 | |
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| 0.0427 | 35.0 | 8750 | 0.0912 | 0.9812 | 0.0417 | 0.2 | 0.0233 | |
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| 0.041 | 36.0 | 9000 | 0.0922 | 0.9808 | 0.0408 | 0.1667 | 0.0233 | |
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| 0.041 | 37.0 | 9250 | 0.0933 | 0.9812 | 0.0417 | 0.2 | 0.0233 | |
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| 0.0404 | 38.0 | 9500 | 0.0929 | 0.9812 | 0.0417 | 0.2 | 0.0233 | |
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| 0.0404 | 39.0 | 9750 | 0.0922 | 0.9816 | 0.0426 | 0.25 | 0.0233 | |
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| 0.0401 | 40.0 | 10000 | 0.0921 | 0.9816 | 0.0426 | 0.25 | 0.0233 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.19.1 |
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