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--- |
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license: mit |
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base_model: cointegrated/rubert-tiny2 |
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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: rubert-tiny2-ner-drugname |
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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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# rubert-tiny2-ner-drugname |
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This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0549 |
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- Precision: 0.7232 |
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- Recall: 0.7690 |
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- F1: 0.7454 |
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- Accuracy: 0.9883 |
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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: 0.0002 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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: 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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| No log | 1.0 | 61 | 0.0493 | 0.6413 | 0.7468 | 0.6901 | 0.9833 | |
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| No log | 2.0 | 122 | 0.0417 | 0.6406 | 0.8291 | 0.7228 | 0.9855 | |
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| No log | 3.0 | 183 | 0.0387 | 0.7588 | 0.7468 | 0.7528 | 0.9879 | |
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| No log | 4.0 | 244 | 0.0396 | 0.7385 | 0.7595 | 0.7488 | 0.9883 | |
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| No log | 5.0 | 305 | 0.0425 | 0.6897 | 0.7595 | 0.7229 | 0.9874 | |
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| No log | 6.0 | 366 | 0.0465 | 0.6991 | 0.7722 | 0.7338 | 0.9876 | |
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| No log | 7.0 | 427 | 0.0487 | 0.7062 | 0.7911 | 0.7463 | 0.9877 | |
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| No log | 8.0 | 488 | 0.0521 | 0.7076 | 0.7658 | 0.7356 | 0.9882 | |
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| 0.0306 | 9.0 | 549 | 0.0540 | 0.7262 | 0.7722 | 0.7485 | 0.9883 | |
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| 0.0306 | 10.0 | 610 | 0.0549 | 0.7232 | 0.7690 | 0.7454 | 0.9883 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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