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--- |
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license: cc-by-4.0 |
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base_model: allegro/herbert-large-cased |
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tags: |
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- generated_from_trainer |
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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: herbert-large-cased_ner |
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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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# herbert-large-cased_ner |
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This model is a fine-tuned version of [allegro/herbert-large-cased](https://huggingface.co/allegro/herbert-large-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3281 |
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- Precision: 0.9354 |
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- Recall: 0.9326 |
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- F1: 0.9337 |
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- Accuracy: 0.9598 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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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: 10 |
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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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| No log | 1.0 | 438 | 0.2556 | 0.8915 | 0.8923 | 0.8918 | 0.9369 | |
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| 0.311 | 2.0 | 876 | 0.1920 | 0.9101 | 0.9107 | 0.9102 | 0.9473 | |
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| 0.1466 | 3.0 | 1314 | 0.2481 | 0.9050 | 0.9058 | 0.9048 | 0.9442 | |
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| 0.093 | 4.0 | 1752 | 0.2565 | 0.9187 | 0.9276 | 0.9229 | 0.9537 | |
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| 0.0584 | 5.0 | 2190 | 0.2620 | 0.9216 | 0.9306 | 0.9260 | 0.9543 | |
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| 0.037 | 6.0 | 2628 | 0.2891 | 0.9263 | 0.9310 | 0.9282 | 0.9533 | |
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| 0.0169 | 7.0 | 3066 | 0.3159 | 0.9288 | 0.9314 | 0.9300 | 0.9564 | |
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| 0.0123 | 8.0 | 3504 | 0.3317 | 0.9359 | 0.9348 | 0.9345 | 0.9606 | |
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| 0.0123 | 9.0 | 3942 | 0.3097 | 0.9357 | 0.9305 | 0.9327 | 0.9594 | |
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| 0.0048 | 10.0 | 4380 | 0.3281 | 0.9354 | 0.9326 | 0.9337 | 0.9598 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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