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license: mit |
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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: deberta-v3-large-finetuned-ner-10epochs-V2 |
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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-large-finetuned-ner-10epochs-V2 |
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1180 |
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- Precision: 0.9033 |
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- Recall: 0.9347 |
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- F1: 0.9187 |
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- Accuracy: 0.9813 |
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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: 8 |
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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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| 0.0663 | 1.0 | 2261 | 0.0715 | 0.8709 | 0.9194 | 0.8945 | 0.9787 | |
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| 0.0583 | 2.0 | 4522 | 0.0629 | 0.8845 | 0.9267 | 0.9051 | 0.9800 | |
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| 0.0442 | 3.0 | 6783 | 0.0635 | 0.8841 | 0.9404 | 0.9114 | 0.9802 | |
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| 0.0402 | 4.0 | 9044 | 0.0588 | 0.9011 | 0.9283 | 0.9145 | 0.9821 | |
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| 0.0327 | 5.0 | 11305 | 0.0676 | 0.8919 | 0.9385 | 0.9146 | 0.9818 | |
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| 0.0245 | 6.0 | 13566 | 0.0713 | 0.9037 | 0.9331 | 0.9182 | 0.9821 | |
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| 0.0183 | 7.0 | 15827 | 0.0848 | 0.9049 | 0.9181 | 0.9114 | 0.9812 | |
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| 0.0157 | 8.0 | 18088 | 0.0898 | 0.8957 | 0.9411 | 0.9178 | 0.9818 | |
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| 0.009 | 9.0 | 20349 | 0.1027 | 0.8965 | 0.9385 | 0.9170 | 0.9817 | |
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| 0.0068 | 10.0 | 22610 | 0.1180 | 0.9033 | 0.9347 | 0.9187 | 0.9813 | |
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### Framework versions |
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- Transformers 4.30.1 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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