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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- generator |
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model-index: |
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- name: deberta-v3-base-finetuned-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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# deberta-v3-base-finetuned-ner |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9895 |
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- Overall Precision: 0.5201 |
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- Overall Recall: 0.3319 |
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- Overall F1: 0.4052 |
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- Overall Accuracy: 0.9326 |
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- Datasetname F1: 0.4952 |
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- Hyperparametername F1: 0.48 |
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- Hyperparametervalue F1: 0.5 |
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- Methodname F1: 0.3933 |
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- Metricname F1: 0.2488 |
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- Metricvalue F1: 0.2456 |
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- Taskname F1: 0.6393 |
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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: 4 |
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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: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Datasetname F1 | Hyperparametername F1 | Hyperparametervalue F1 | Methodname F1 | Metricname F1 | Metricvalue F1 | Taskname F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:---------------------:|:----------------------:|:-------------:|:-------------:|:--------------:|:-----------:| |
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| No log | 1.0 | 141 | 1.2556 | 0.2784 | 0.1520 | 0.1967 | 0.9212 | 0.0 | 0.3478 | 0.2581 | 0.3750 | 0.0 | 0.0 | 0.0556 | |
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| No log | 2.0 | 282 | 0.8945 | 0.3020 | 0.5096 | 0.3793 | 0.9088 | 0.5 | 0.1538 | 0.2778 | 0.3540 | 0.4566 | 0.0896 | 0.3756 | |
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| No log | 3.0 | 423 | 1.0233 | 0.3702 | 0.4518 | 0.4069 | 0.9268 | 0.4211 | 0.2647 | 0.3333 | 0.3529 | 0.4658 | 0.1613 | 0.5270 | |
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| 0.6352 | 4.0 | 564 | 1.1734 | 0.4316 | 0.4390 | 0.4352 | 0.9310 | 0.4854 | 0.3462 | 0.3415 | 0.4352 | 0.4269 | 0.2295 | 0.5827 | |
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| 0.6352 | 5.0 | 705 | 1.3147 | 0.4840 | 0.4540 | 0.4685 | 0.9390 | 0.5143 | 0.5 | 0.625 | 0.5739 | 0.3495 | 0.2333 | 0.5865 | |
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| 0.6352 | 6.0 | 846 | 2.1441 | 0.5618 | 0.3405 | 0.4240 | 0.9373 | 0.5185 | 0.5581 | 0.6061 | 0.4898 | 0.2365 | 0.1071 | 0.6126 | |
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| 0.6352 | 7.0 | 987 | 1.9895 | 0.5201 | 0.3319 | 0.4052 | 0.9326 | 0.4952 | 0.48 | 0.5 | 0.3933 | 0.2488 | 0.2456 | 0.6393 | |
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### Framework versions |
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- Transformers 4.23.1 |
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- Pytorch 1.12.1+cu102 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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