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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_1_1_ext_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: 0.8383838383838383 |
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- name: Recall |
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type: recall |
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value: 0.8872260823089257 |
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- name: F1 |
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type: f1 |
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value: 0.8621137366917683 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9569787813899163 |
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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_1_1_ext_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: 0.2513 |
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- Precision: 0.8384 |
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- Recall: 0.8872 |
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- F1: 0.8621 |
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- Accuracy: 0.9570 |
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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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| 0.3012 | 3.42 | 500 | 0.1677 | 0.8115 | 0.8626 | 0.8363 | 0.9518 | |
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| 0.1081 | 6.85 | 1000 | 0.1869 | 0.8218 | 0.8749 | 0.8475 | 0.9548 | |
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| 0.0654 | 10.27 | 1500 | 0.2132 | 0.8311 | 0.8813 | 0.8555 | 0.9559 | |
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| 0.0449 | 13.7 | 2000 | 0.2284 | 0.8296 | 0.8797 | 0.8540 | 0.9559 | |
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| 0.0341 | 17.12 | 2500 | 0.2353 | 0.8348 | 0.8856 | 0.8594 | 0.9575 | |
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| 0.0267 | 20.55 | 3000 | 0.2413 | 0.8397 | 0.8872 | 0.8628 | 0.9581 | |
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| 0.0227 | 23.97 | 3500 | 0.2513 | 0.8384 | 0.8872 | 0.8621 | 0.9570 | |
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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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