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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/deberta-v3-base |
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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-base-financial-inc-dec-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-financial-inc-dec-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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0416 |
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- Precision: 0.9632 |
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- Recall: 0.9704 |
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- F1: 0.9668 |
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- Accuracy: 0.9933 |
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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: 1e-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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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 15 |
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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 | 92 | 0.1193 | 0.625 | 0.7407 | 0.6780 | 0.9588 | |
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| No log | 2.0 | 184 | 0.0522 | 0.8643 | 0.8963 | 0.88 | 0.9798 | |
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| No log | 3.0 | 276 | 0.0554 | 0.8897 | 0.8963 | 0.8930 | 0.9835 | |
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| No log | 4.0 | 368 | 0.0362 | 0.9416 | 0.9556 | 0.9485 | 0.9910 | |
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| No log | 5.0 | 460 | 0.0315 | 0.9286 | 0.9630 | 0.9455 | 0.9918 | |
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| 0.1731 | 6.0 | 552 | 0.0416 | 0.9632 | 0.9704 | 0.9668 | 0.9933 | |
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| 0.1731 | 7.0 | 644 | 0.0496 | 0.9420 | 0.9630 | 0.9524 | 0.9910 | |
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| 0.1731 | 8.0 | 736 | 0.0527 | 0.9420 | 0.9630 | 0.9524 | 0.9910 | |
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| 0.1731 | 9.0 | 828 | 0.0604 | 0.9348 | 0.9556 | 0.9451 | 0.9895 | |
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| 0.1731 | 10.0 | 920 | 0.0564 | 0.9420 | 0.9630 | 0.9524 | 0.9910 | |
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| 0.0028 | 11.0 | 1012 | 0.0571 | 0.9493 | 0.9704 | 0.9597 | 0.9918 | |
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| 0.0028 | 12.0 | 1104 | 0.0570 | 0.9493 | 0.9704 | 0.9597 | 0.9918 | |
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| 0.0028 | 13.0 | 1196 | 0.0559 | 0.9493 | 0.9704 | 0.9597 | 0.9918 | |
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| 0.0028 | 14.0 | 1288 | 0.0574 | 0.9493 | 0.9704 | 0.9597 | 0.9918 | |
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| 0.0028 | 15.0 | 1380 | 0.0576 | 0.9493 | 0.9704 | 0.9597 | 0.9918 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu124 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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