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--- |
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base_model: UWB-AIR/Czert-B-base-cased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cnec |
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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: CNEC_2_0_Czert-B-base-cased |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: cnec |
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type: cnec |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8093464273620048 |
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- name: Recall |
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type: recall |
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value: 0.8547925608011445 |
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- name: F1 |
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type: f1 |
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value: 0.8314489476430683 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9446311123820418 |
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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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# CNEC_2_0_Czert-B-base-cased |
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This model is a fine-tuned version of [UWB-AIR/Czert-B-base-cased](https://huggingface.co./UWB-AIR/Czert-B-base-cased) on the cnec dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3352 |
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- Precision: 0.8093 |
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- Recall: 0.8548 |
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- F1: 0.8314 |
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- Accuracy: 0.9446 |
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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: 32 |
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- eval_batch_size: 32 |
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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: 25 |
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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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| 0.5496 | 2.22 | 500 | 0.2782 | 0.7301 | 0.7750 | 0.7519 | 0.9275 | |
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| 0.2133 | 4.44 | 1000 | 0.2487 | 0.7811 | 0.8219 | 0.8010 | 0.9399 | |
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| 0.144 | 6.67 | 1500 | 0.2580 | 0.7737 | 0.8290 | 0.8004 | 0.9396 | |
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| 0.1029 | 8.89 | 2000 | 0.2576 | 0.7997 | 0.8480 | 0.8231 | 0.9446 | |
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| 0.0776 | 11.11 | 2500 | 0.2849 | 0.7990 | 0.8516 | 0.8244 | 0.9444 | |
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| 0.0601 | 13.33 | 3000 | 0.2971 | 0.8021 | 0.8523 | 0.8264 | 0.9450 | |
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| 0.0494 | 15.56 | 3500 | 0.3077 | 0.8014 | 0.8473 | 0.8237 | 0.9440 | |
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| 0.0408 | 17.78 | 4000 | 0.3145 | 0.8131 | 0.8555 | 0.8337 | 0.9448 | |
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| 0.0353 | 20.0 | 4500 | 0.3260 | 0.8097 | 0.8569 | 0.8327 | 0.9445 | |
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| 0.0311 | 22.22 | 5000 | 0.3356 | 0.8076 | 0.8541 | 0.8302 | 0.9441 | |
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| 0.0281 | 24.44 | 5500 | 0.3352 | 0.8093 | 0.8548 | 0.8314 | 0.9446 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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