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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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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: adr-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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# adr-ner |
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This model is a fine-tuned version of [austin/Austin-MeDeBERTa](https://huggingface.co/austin/Austin-MeDeBERTa) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0434 |
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- Precision: 0.7305 |
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- Recall: 0.6934 |
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- F1: 0.7115 |
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- Accuracy: 0.9941 |
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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: 12 |
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- eval_batch_size: 12 |
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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: 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 | 107 | 0.0630 | 0.0 | 0.0 | 0.0 | 0.9876 | |
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| No log | 2.0 | 214 | 0.0308 | 0.4282 | 0.3467 | 0.3832 | 0.9900 | |
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| No log | 3.0 | 321 | 0.0254 | 0.5544 | 0.5603 | 0.5573 | 0.9920 | |
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| No log | 4.0 | 428 | 0.0280 | 0.6430 | 0.5751 | 0.6071 | 0.9929 | |
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| 0.0465 | 5.0 | 535 | 0.0266 | 0.5348 | 0.7146 | 0.6118 | 0.9915 | |
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| 0.0465 | 6.0 | 642 | 0.0423 | 0.7632 | 0.5793 | 0.6587 | 0.9939 | |
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| 0.0465 | 7.0 | 749 | 0.0336 | 0.6957 | 0.6765 | 0.6860 | 0.9939 | |
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| 0.0465 | 8.0 | 856 | 0.0370 | 0.6876 | 0.6702 | 0.6788 | 0.9936 | |
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| 0.0465 | 9.0 | 963 | 0.0349 | 0.6555 | 0.7040 | 0.6789 | 0.9932 | |
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| 0.0044 | 10.0 | 1070 | 0.0403 | 0.6910 | 0.6808 | 0.6858 | 0.9938 | |
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| 0.0044 | 11.0 | 1177 | 0.0415 | 0.7140 | 0.6808 | 0.6970 | 0.9939 | |
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| 0.0044 | 12.0 | 1284 | 0.0440 | 0.7349 | 0.6681 | 0.6999 | 0.9941 | |
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| 0.0044 | 13.0 | 1391 | 0.0423 | 0.7097 | 0.6977 | 0.7036 | 0.9941 | |
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| 0.0044 | 14.0 | 1498 | 0.0435 | 0.7174 | 0.6977 | 0.7074 | 0.9941 | |
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| 0.0006 | 15.0 | 1605 | 0.0434 | 0.7305 | 0.6934 | 0.7115 | 0.9941 | |
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### Framework versions |
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- Transformers 4.14.1 |
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- Pytorch 1.10.0+cu113 |
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- Datasets 1.16.1 |
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- Tokenizers 0.10.3 |
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