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--- |
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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-ner-B |
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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-ner-B |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on English part of [Babelscape/multinerd](https://huggingface.co/datasets/Babelscape/multinerd) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0294 |
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- Precision: 0.9660 |
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- Recall: 0.9751 |
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- F1: 0.9705 |
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- Accuracy: 0.9929 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 128 |
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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: 3 |
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- mixed_precision_training: Native AMP |
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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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| 0.0336 | 0.13 | 257 | 0.0345 | 0.9245 | 0.9386 | 0.9315 | 0.9885 | |
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| 0.0309 | 0.25 | 514 | 0.0296 | 0.9161 | 0.9624 | 0.9387 | 0.9892 | |
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| 0.0281 | 0.38 | 771 | 0.0251 | 0.9316 | 0.9539 | 0.9426 | 0.9908 | |
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| 0.0221 | 0.5 | 1028 | 0.0259 | 0.9381 | 0.9588 | 0.9483 | 0.9910 | |
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| 0.0234 | 0.63 | 1285 | 0.0260 | 0.9318 | 0.9640 | 0.9477 | 0.9904 | |
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| 0.0177 | 0.75 | 1542 | 0.0248 | 0.9331 | 0.9665 | 0.9495 | 0.9909 | |
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| 0.0213 | 0.88 | 1799 | 0.0228 | 0.9522 | 0.9593 | 0.9557 | 0.9918 | |
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| 0.0252 | 1.0 | 2056 | 0.0233 | 0.9517 | 0.9568 | 0.9542 | 0.9917 | |
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| 0.0143 | 1.13 | 2313 | 0.0256 | 0.9491 | 0.9641 | 0.9565 | 0.9918 | |
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| 0.0132 | 1.25 | 2570 | 0.0247 | 0.9536 | 0.9627 | 0.9581 | 0.9921 | |
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| 0.015 | 1.38 | 2827 | 0.0243 | 0.9467 | 0.9640 | 0.9553 | 0.9917 | |
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| 0.0148 | 1.5 | 3084 | 0.0254 | 0.9475 | 0.9677 | 0.9575 | 0.9918 | |
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| 0.0143 | 1.63 | 3341 | 0.0252 | 0.9491 | 0.9667 | 0.9578 | 0.9920 | |
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| 0.0112 | 1.75 | 3598 | 0.0244 | 0.9546 | 0.9626 | 0.9586 | 0.9923 | |
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| 0.0074 | 1.88 | 3855 | 0.0268 | 0.9490 | 0.9680 | 0.9584 | 0.9921 | |
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| 0.0068 | 2.0 | 4112 | 0.0257 | 0.9577 | 0.9610 | 0.9594 | 0.9923 | |
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| 0.0079 | 2.13 | 4369 | 0.0296 | 0.9457 | 0.9698 | 0.9576 | 0.9919 | |
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| 0.0067 | 2.26 | 4626 | 0.0290 | 0.9520 | 0.9686 | 0.9602 | 0.9922 | |
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| 0.0067 | 2.38 | 4883 | 0.0282 | 0.9553 | 0.9653 | 0.9603 | 0.9923 | |
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| 0.0044 | 2.51 | 5140 | 0.0303 | 0.9600 | 0.9622 | 0.9611 | 0.9926 | |
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| 0.005 | 2.63 | 5397 | 0.0318 | 0.9488 | 0.9703 | 0.9594 | 0.9920 | |
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| 0.006 | 2.76 | 5654 | 0.0295 | 0.9564 | 0.9663 | 0.9613 | 0.9925 | |
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| 0.0059 | 2.88 | 5911 | 0.0304 | 0.9586 | 0.9657 | 0.9621 | 0.9925 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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