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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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- accuracy |
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- f1 |
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- recall |
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model-index: |
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- name: camembert-base-finetuned-ICDCode_5 |
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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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# camembert-base-finetuned-ICDCode_5 |
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This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. It has been trained on a corpus of death certificate. One ICDCode is given for a given cause of death or commorbidities. As it is an important task to be able to predict these ICDCode, I shave trained this model for 8 epochs on 400 000 death causes. Pre-processing of noisy data points was mandatory before tokenization. It allows us to get this accuracy. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6574 |
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- Accuracy: 0.8964 |
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- F1: 0.8750 |
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- Recall: 0.8964 |
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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: 2e-05 |
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- train_batch_size: 50 |
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- eval_batch_size: 50 |
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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: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:| |
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| 3.7466 | 1.0 | 4411 | 1.9448 | 0.7201 | 0.6541 | 0.7201 | |
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| 1.5264 | 2.0 | 8822 | 1.2045 | 0.8134 | 0.7691 | 0.8134 | |
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| 1.0481 | 3.0 | 13233 | 0.9473 | 0.8513 | 0.8149 | 0.8513 | |
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| 0.8304 | 4.0 | 17644 | 0.8098 | 0.8718 | 0.8427 | 0.8718 | |
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| 0.7067 | 5.0 | 22055 | 0.7352 | 0.8834 | 0.8574 | 0.8834 | |
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| 0.6285 | 6.0 | 26466 | 0.6911 | 0.8898 | 0.8659 | 0.8898 | |
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| 0.5779 | 7.0 | 30877 | 0.6641 | 0.8958 | 0.8741 | 0.8958 | |
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| 0.549 | 8.0 | 35288 | 0.6574 | 0.8964 | 0.8750 | 0.8964 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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