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--- |
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base_model: microsoft/mdeberta-v3-base |
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library_name: transformers |
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license: mit |
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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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tags: |
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- generated_from_trainer |
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model-index: |
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- name: scenario-kd-pre-ner-full-mdeberta_data-univner_en55 |
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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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# scenario-kd-pre-ner-full-mdeberta_data-univner_en55 |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 62.9050 |
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- Precision: 0.7596 |
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- Recall: 0.7360 |
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- F1: 0.7476 |
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- Accuracy: 0.9801 |
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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: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 32 |
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- seed: 55 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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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| 148.0892 | 1.2755 | 500 | 103.3325 | 0.3059 | 0.2578 | 0.2798 | 0.9556 | |
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| 87.0932 | 2.5510 | 1000 | 80.2722 | 0.6704 | 0.6739 | 0.6722 | 0.9755 | |
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| 72.3221 | 3.8265 | 1500 | 72.3381 | 0.7265 | 0.7039 | 0.7150 | 0.9775 | |
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| 65.7687 | 5.1020 | 2000 | 68.3339 | 0.7549 | 0.7174 | 0.7357 | 0.9783 | |
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| 61.9669 | 6.3776 | 2500 | 65.6428 | 0.7442 | 0.7319 | 0.7380 | 0.9789 | |
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| 59.6427 | 7.6531 | 3000 | 64.0535 | 0.7581 | 0.7267 | 0.7421 | 0.9798 | |
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| 58.1252 | 8.9286 | 3500 | 62.9050 | 0.7596 | 0.7360 | 0.7476 | 0.9801 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.19.1 |
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