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--- |
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license: mit |
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base_model: microsoft/deberta-v3-small |
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tags: |
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- generated_from_trainer |
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metrics: |
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- recall |
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- precision |
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model-index: |
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- name: deberta-v3-small-finetuned-ner-2048 |
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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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# deberta-v3-small-finetuned-ner-2048 |
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This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0036 |
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- Recall: 0.9920 |
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- Precision: 0.9866 |
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- Fbeta Score: 0.9918 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 4 |
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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_ratio: 0.1 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Recall | Precision | Fbeta Score | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:-----------:| |
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| 0.0085 | 1.0 | 3186 | 0.0058 | 0.9786 | 0.9693 | 0.9782 | |
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| 0.0032 | 2.0 | 6373 | 0.0036 | 0.9869 | 0.9764 | 0.9865 | |
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| 0.004 | 3.0 | 9559 | 0.0037 | 0.9791 | 0.9892 | 0.9795 | |
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| 0.0014 | 4.0 | 12746 | 0.0035 | 0.9908 | 0.9817 | 0.9905 | |
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| 0.0019 | 5.0 | 15932 | 0.0038 | 0.9903 | 0.9806 | 0.9899 | |
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| 0.0022 | 6.0 | 19119 | 0.0032 | 0.9929 | 0.9861 | 0.9927 | |
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| 0.0005 | 7.0 | 22305 | 0.0031 | 0.9906 | 0.9894 | 0.9905 | |
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| 0.0003 | 8.0 | 25492 | 0.0033 | 0.9915 | 0.9855 | 0.9913 | |
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| 0.0001 | 9.0 | 28678 | 0.0036 | 0.9920 | 0.9866 | 0.9918 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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